Browse Topic: Fault detection

Items (357)

A Comparative Study on fault diagnosis of Electrical vehicle motor testing machine Gear Components Using machine Learning algorithms

2025-28-0177To be published on 02/07/2025

Electric vehicles (EVs) are the way of the future for the automobile industry. The drive motor is a vital part of modern EVs. A motor testing machine is used to verify the critical parameters of the electrical motor, which is seen to be of the utmost importance. Electric motor characteristic curve points and their electromagnetic behaviour under different conditions are regularly found using the motor testing machine. But it has been noted that a flaw in the testing machine's helical gear arrangement causes frequent malfunctions and less-than-ideal performance, which impacts the assembly line's effectiveness in producing electric cars. The research project is to enhance the motor test bench apparatus by modifying crucial design parameters using machine learning and vibration signal analysis methods. Vibration signals are recorded at various gear settings before statistical features are extracted. Following that, these signals are classified using classifiers such Quadratic SVM and

S PhD, Ravikumar, V, Muralidharan, D, Pradeep Kumar

Anomaly Detection in Fleet Vehicle Data and Statistical Approach to Develop Engine System OBD Thresholds

2024-28-0195

To be published on 12/05/2024

On-Board-Diagnostics (OBD) are crucial for ensuring the proper functioning of Engine’s emission control system by continuously monitoring various sensors and components. When the failure is detected, the Check Engine Light is triggered on Vehicle’s dashboard, alerting the driver to seek professional service to address the issue. However, the task of developing the monitoring strategies and performing robust calibration is challenging and time consuming. Model in loop (MIL) Simulation and testing is a technique used to understand and estimate the behavior of a system or sub system. The diagnostics model can be tested and refined within the model-based environment allowing a complex system to be efficiently regulated. MIL framework could be explored at various stages of development from early in the design phase to later stages of series developments through vehicle fleet data. This framework allows early identification and correction of errors and bugs in a standalone dependent

Kumar, Amit, Hegde, Karthik, Challa, Krishna, H, YASHWANTH

Safe Phase Current Measurement for Motor Control Applications

2024-28-0141

To be published on 12/05/2024

As the automotive industry progresses towards electrification, driven by need for sustainability and reduced emissions, the traction inverter emerges as pivotal component of electric vehicles (EVs). Serving as the interface between the vehicle’s control systems, motor and battery. The traction inverter’s performance directly impacts the efficiency, sustainability and overall functionality of electric drive systems. A critical function of the traction inverter is measurement of phase currents in each motor phase, enabling precise control of the motor’s torque and rational speed. This capability is essential for optimizing efficiency, enhancing performance and ensuring safety key aspects of modern electric vehicle technology. This paper introduces method for measuring phase currents in Permanent Magnet Synchronous Motors (PMSM) utilizing the Enhanced Versitile Analog-Digital-Converter (EVADC) integrated within Infineon’s Aurix Tricore. This technology preferred for its rapid conversion

Birari, Ashwini Anil

A FAULT-TOLERANT HYBRID ELECTRIC VEHICLE POWERTRAIN

2024-01-3418

11/15/2024

Abstract This paper presents a fault-tolerant powertrain topology for series hybrid electric vehicles (SHEVs). The introduction of a redundant phase leg that is shared by three converters in a standard SHEV drive system allows to maximize the reliability improvement with minimal part-count increase. The new topology features fast response in fault detection and isolation, and post-fault operation at rated power throughput. The operating principle, control strategy, and fault diagnostic methods are elaborated. The substantially improved reliability over the standard topology is verified by the Markov reliability model. Time-domain simulation based on a Saber model has been conducted and the results have verified the feasibility and performance of the proposed SHEV drive system with fault-tolerant capability. The experimental results from a prototype have further validated the robust fault detection scheme and excellent post-fault performance

Song, Yantao, Wang, Bingsen

Equalization System Engineering Design for reliable Battery Management on eVTOL

2024-01-7027

11/15/2024

Electrical vertical take-off and landing vehicle (eVTOL) are more and more popular in future’s urban mobility. How to improve the reliability of the battery, is the key problem. Battery Management System (BMS) through the battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to improve the battery reliability and performance, of which battery equalisation technology plays a vital role. BMS manages batteries through battery status monitoring, charging and discharging control, temperature management, fault diagnosis, battery equalisation and other core measures to ensure the safety, reliability and performance of batteries. This paper analyses the inconsistency mechanism of batteries, introduces the classification of mainstream balancing circuits, describes the advantages and disadvantages of different types of balancing technologies, introduces the practical application scheme of passive

Feng, Guo, Zhang, Xinfeng, Li, Hong Dun, Yue, Han

Knowledge Push Mechanism of Rolling Bearing Fault Diagnosis Employing Deep Residual Network Architecture

2024-01-6004

09/11/2024

Rolling bearings play a critical role in rotating machinery, with their fatigue life directly impacting equipment’s operational reliability. This underscores the significant engineering application value of “fault diagnosis” (FD) technology for rolling bearings in mechanical, automation, and aerospace domains. Literature reviews highlight that a substantial portion of failures in machinery such as jet turbine engines, wind turbines, gear reducers, and induction machines are attributable to bearing issues. Early fault detection and preventive maintenance are therefore imperative for ensuring the smooth operation of rotating machinery. This paper focuses on rolling bearings, delving deep into FD technology using machine learning principles. It analyses the structure and common failure modes of rolling bearings, discussing an FD method based on machine learning. Specifically, the SE-DRN (“squeeze-exclusion deep residual network”) approach is employed, leveraging “variational modal

Muin, Abdullah-Al, Khan, Shahrukh, Miah, Md Helal

Fault Detection in Machine Bearings Using Deep Learning

2024-26-0473

06/01/2024

In the contemporary industrial landscape, machinery stands as the cornerstone of various sectors. Over time, these machines undergo wear and tear due to extensive use, leading to the introduction of subtle faults into the machine readings. Recognizing the pivotal role of machinery in diverse industries, the timely detection of these faults becomes imperative. Early fault detection is crucial for preventing costly downtimes, ensuring operational efficiency, and enhancing overall safety. This paper addresses the need for an effective condition monitoring and fault detection system, focusing specifically on the application of the Long Short-Term Memory (LSTM) deep learning model for fault detection in bearings using accelerometer data. The preprocessing phase involves extracting time domain features, encompassing normal, differentiated, integrated, and carefully selected signals, to create an informative dataset tailored for the LSTM model. This model is then meticulously trained on the

Vaishnavi, A., Sharma, Anju, Naidu, VPS

Formal Technique for Fault Detection and Identification of Control Intensive Application of Stall Warning System Using System Theoretic Process Analysis

2024-26-0471

06/01/2024

Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft. Traditional Functional Hazard and Fault Tree analyses are inadequate to capture all failures due to the complex hardware-software interactions for stall warning and protection system. Hence, an improved methodology for failure detection and identification is proposed. This paper discusses a hybrid formal method and model-based technique using System Theoretic Process Analysis (STPA) to identify and diagnose faults and provide monitors to process the identified faults to ensure robust design of the indigenous stall warning and protection system (SWPS). The

Kale, Alexander, Madhuranath, Ganesh, Shanmugham, Viswanathan, Nanda, Manju, Singh, Giresh, Durak, Umut

Deep Learning-Based Digital Twining Models for Inter System Behavior and Health Assessment of Combat Aircraft Systems

2024-26-0478

06/01/2024

Modern combat aircraft demands efficient maintenance strategies to ensure operational readiness while minimizing downtime and costs. Innovative approaches using Digital Twining models are being explored to capture inter system behaviors and assessing health of systems which will help maintenance aspects. This approach employs advanced deep learning protocols to analyze the intricate interactions among various systems using the data collected from various systems. The research involves extensive data collection from sensors within combat aircraft, followed by data preprocessing and feature selection, using domain knowledge and correlation analysis. Neural networks are designed for individual systems, and hyper parameter tuning is performed to optimize their performance. By combining those outputs during the model integration phase, an overall health assessment of the aircraft can be generated. This assessment enables advanced fault isolation at the system level by identifying subtle

Agrawal, Ankur, Farid, Fahad, Prabhu, Aniruddh, Sudhakar, Veluri, Vyas, Nalinaksh

Application of Motor Load Emulation Techniques for EPB (Electric Parking Brake) System Tests

2024-01-2274

04/09/2024

The EPB (Electric Parking Brake) system is divided into two parts based on VDA305-100 recommendation (German Association of the Automotive Industry, VDA). One part of the EPB system contains the parking brake actuator, caliper, and actuation logic (parking brake controller, PBC). The second part of the EPB system is called to the HOST which contains the EPB power electronics, necessary peripherals and controls the functions that the driver can experience. According to VDA305-100, the PBC is responsible for recognition of a fault in the parking brake actuator based on the measured values transmitted from the HOST such as EPB motor voltage and current. Due to mechanical fault injection limitations, failsafe tests require physically electrical emulation caused by parking brake actuator faults to verify the parking brake actuator fault detection and management algorithm. This paper introduces EPB motor load emulation techniques in which EPB HILS (Hardware in the Loop Simulation) test

Son, Changhyun, Yu, Hyunuk

Machine Learning Approach for Open Circuit Fault Detection and Localization in EV Motor Drive Systems

2024-01-2790

04/09/2024

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network. This recurrent neural network (RNN)-based ML model as a multiclass classifier localizes the

Arsalan, Ali, Papari, Behnaz, Rahman, S M Imrat, Timilsina, Laxman, Moghassemi, Ali, Muriithi, Grace, Ozkan, Gokhan, Edrington, Christopher, Buraimoh, Elutunji

Improving CRC Fault Detection Probability in AUTOSAR E2E Based on Known Hamming Weights

2024-01-1987

04/09/2024

To develop safe vehicles, system development must be performed in compliance with functional safety. Functional safety considers situations where failures could make a vehicle unsafe, and it requires the inclusion of mechanisms to detect and mitigate these failures, even though they may not always be detected with 100% certainty — referred as diagnostic coverage (DC). Therefore, some faults, called residual faults, might go undetected. In the realm of functional safety from a communication perspective, industry standards define nine distinct fault modes. The detection of these faults is crucial, especially in the widely used AUTOSAR automotive operating system. AUTOSAR E2E (End-to-End Communication Protection) serves as a communication fault detection mechanism utilizing three mechanisms: counters, timers, and Cyclic Redundancy Check (CRC) to address the nine fault modes. Especially, determining the DC for CRC can be challenging and often requires a conservative evaluation approach. In

Emi, Taichi, Aung, Han Nay, Yamasaki, Yasuhiro, Ohsaki, Hiroyuki

Vehicle Control Development - Converting a Medium-Duty Commercial Truck into a Battery Electric Vehicle

2024-01-2047

04/09/2024

The transition towards electrification in commercial vehicles has received more attention in recent years. This paper details the conversion of a production Medium-Duty class-5 commercial truck, originally equipped with a gasoline engine and 10-speed automatic transmission, into a battery electric vehicle (BEV). The conversion process involved the removal of the internal combustion engine, transmission, and differential unit, followed by the integration of an ePropulsion system, including a newly developed dual-motor beam axle that propels the rear wheels. Other systems added include an 800V/99 kWh battery pack, advanced silicon carbide (SiC) inverters, an upgraded thermal management system, and a DC fast charging system. A key part of the work was the development of the propulsion system controls, which prioritized drivability, NVH suppression, and energy optimization. The improvement of the electrified truck compared to the gasoline version in responsiveness and reduced noise

Liu, Xiaobing, Guo, Chengyun, Rama, Neeraj, Theunissen, Frans, Olin, Pete, Ling, Gang, Pan, Yang, Mohon, Sara, Van Maanen, Keith, Chen, Wei

Steering Angle Safety Control for Redundant Steering System Considering Motor Winding’s Various Faults

2024-01-2520

04/09/2024

Reliable and safe Redundant Steering System (RSS) equipped with Dual-Winding Permanent Magnet Synchronous Motor (DW-PMSM) is considered an ideal actuator for future autonomous vehicle chassis. The built-in DW-PMSM of the RSS is required to identify various winding’s faults such as disconnection, open circuit, and grounding. When achieving redundant control through winding switching, it is necessary to suppress speed fluctuations during the process of winding switching to ensure angle control precision. In this paper, a steering angle safety control for RSS considering motor winding’s faults is proposed. First, we analyze working principle of RSS. Corresponding steering system model and fault model of DW-PMSM have been established. Next, we design the fault diagnosis and fault tolerance strategy of RSS. Considering the difference in amplitude frequency characteristics of phase current during DW-PMSM winding faults, the Hanning window and Short-Time Fourier Transform (STFT) is

Zhao, Jian, Dang, Ruijie, Wu, Hangzhe, Zhu, Bing, Chen, Zhicheng

Implementation of Machine Learning in Acoustics Source Detection by Leveraging Synthetic Sound Data Generation Approach

2024-26-0213

01/16/2024

E-Mobility and low noise IC Engines has pushed product development teams to focus more on sound quality rather than just on reduced noise levels and legislative needs. Furthermore, qualification of products from a sound quality perspective from an end of line testing requirement is also a major challenge. End of line (EOL) NVH testing is key evaluation criteria for product quality with respect to NVH and warranty. Currently for subsystem or component level evaluation, subjective assessment of the components is done by a person to segregate OK and NOK components. As human factor is included, the process becomes very subjective and time consuming. Components with different acceptance criteria will be present and it’s difficult to point out the root cause for NOK components. In this paper, implementation of machine learning is done for acoustic source detection at end of line testing. To improve the fault detection an automated intelligent tool has been developed for subjective to

Shukle, Srinidhi, Iyer, Ganesh, Faizan, Mohammed

Intelligent Diagnosis for Fuel Line Fault of Diesel Engine Based on Vibration Signatures

2024-26-0221

01/16/2024

Early fault detection is vital in maintaining system stability and to decrease the cost associated with maintenance. This paper presents an approach to identify the fuel line failure for a diesel engine based on vibration signals and machine learning. Vibration measurements are performed on the fuel line of the engine for both normal and faulty conditions for engine ramp up condition. After acquiring the time domain vibration signals, various features were extracted and have been analyzed in time and time-frequency domains. Based on the most effective feature, a machine learning model (i.e., support vector machine (SVM)) for fault diagnosis is developed. Results showed that the proposed SVM based model can detect the fuel line fault correctly. This study can be useful for early detection of this critical fault in diesel engine and take useful decision before any catastrophic failure happens because of this fault

Chaudhari, Parag, Gangsar, Purushottam, Dharmadhikari, Nitin, Pawar, Sachin, Mandke, Devendra

Material Recognition Technology of Internal Loose Particles in Sealed Electronic Components Based on Random Forest

01-17-02-0009

12/05/2023

Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material

Gao, Yajie, Wang, Guotao, Jiang, Aiping, Yan, Huizhen

Gear Fault Diagnosis for Vehicle Electric Drive Systems Based on Stator Currents

2023-01-7030

10/30/2023

With the popularization of electric vehicles, the safety performance of electric vehicles has drawn much attention. However, the gears of electric vehicles are more prone to failure at high speeds, which can affect the safety performance of the vehicle. This topic proposes a electromechanical coupling model, which is composed of a permanent magnet synchronous motor model, a vehicle longitudinal dynamics model and a transmission system model, and will be applied to gear fault diagnosis. First, the sensitivity of the gear fault to the stator current signal, the electromagnetic torque signal and the q-axis current signal is investigated based on the time-varying meshing stiffness obtained by the potential energy method. The discrete wavelet algorithm is used to decompose the stator current signal, and the d1 component with obvious fault information is obtained. Then, the singular spectral entropy is selected to realize the feature extraction of the stator current signal by comparing the

Gong, Hao, Wang, Feng, Zhu, Xiaoyuan

A Multi-Sine Excitation Signal Optimization Strategy for EIS Measurement of High-Power Fuel Cells

2023-01-7029

10/30/2023

Electrochemical impedance spectroscopy (EIS) is widely used for fuel cell fault diagnosis. However, traditional EIS measurements take a long time and are difficult to use for real-time diagnosis. Using multi-sine composite signals as the excitation source for fuel cell EIS measurements can shorten the measurement time, but the problem of large signal peaks is also introduced. Moreover, for high-power fuel cell systems, the smallest possible excitation amplitude is needed to reduce power fluctuations, but too small an excitation signal amplitude leads to a lower signal-to-noise ratio (SNR) and poor noise immunity. To tackle this challenge, the strategy proposed in this paper is to maximize the amplitude of each individual frequency component while minimizing the peak value of the composite signal. Firstly, the boundary condition is determined as the peak value of the composite signal does not exceed 1% of the DC current, after that, the amplitude of the individual frequency is changed

Ma, Tiancai, Xu, Xinru, Xue, Longchang, Lin, Weikang

Model-Based Fault Diagnostic Strategy for Microgrids

2023-01-1506

09/05/2023

Microgrids are a topic of interest in recent years, largely due to their compatibility with the integration of distributed renewable resources, capability for bidirectional power flow, and ability to reconfigure to mitigate the effects of faults. Fault diagnosis algorithms are a foundational technology for microgrids. These algorithms must have two primary capabilities. First, faults must be detectable; it is known when the fault occurs. Second, faults must be isolable; the type and location of detected faults can be determined. However, most fault handling research considering microgrids has focused on the protection algorithm. Protection algorithms seek to quickly extinguish dangerous faults which can damage components. However, these algorithms may not sufficiently capture less severe faults, or provide comprehensive monitoring for the microgrid. This is particularly relevant when considering applications involving fault tolerant control or dynamic grid reconfiguration. Although

Heyer, Gabriel, D'Arpino, Matilde

792-2 Second-Generation Ku-Band and Ka-Band Satellite Communication System

ARINC792-2 (Current)08/25/2023

This standard sets forth the desired characteristics of a second-generation aviation Ku-band and Ka-band satellite communication (satcom) system intended for installation in all types of aircraft

Airlines Electronic Engineering Committee

Machine-Learning-Based Fault Detection in Electric Vehicle Powertrains Using a Digital Twin

2023-01-1214

06/26/2023

Electric Vehicles are subject to effects that lead to more or less rapid degradation of functions. This can cause hazards for the drivers and uninvolved road participants. For this reason, the must be detected and mitigated, to maintain the vehicle function even in critical situations until a safe operating mode can be established. This publication presents an intelligent digital twin, located in the edge and connected with an electric vehicle via 5G. That can improve the operation of electrified vehicles by enabling the online detection of abnormal situations in the electrified powertrain and vehicle dynamics. Its core component is the fault detection system, which is implemented based on a 1-Nearest Neighbor algorithm. It is initially trained on synthetic data, generated in CarMaker for real-world powertrain issues such as demagnetization and open-/short-switch failures, using detailed mathematical models. In this context 139 simulations were performed with three different velocities

Dettinger, Falk, Jazdi, Nasser, Weyrich, Michael, Brandl, Lukas, Reuss, Hans-Christian, Pecha, Urs, Parspour, Nejila, Li, Shiqing, Frey, Michael, Gauterin, Frank, Nägele, Ann-Therese, Lüntzel, Vitus Alexander, Sax, Eric

Recognition Method for Electronic Component Signals Based on LR-SMOTE and Improved Random Forest Algorithm

01-17-01-0005

06/10/2023

Loose particles are a major problem affecting the performance and safety of aerospace electronic components. The current particle impact noise detection (PIND) method used in these components suffers from two main issues: data collection imbalance and unstable machine-learning-based recognition models that lead to redundant signal misclassification and reduced detection accuracy. To address these issues, we propose a signal identification method using the limited random synthetic minority oversampling technique (LR-SMOTE) for unbalanced data processing and an optimized random forest (RF) algorithm to detect loose particles. LR-SMOTE expands the generation space beyond the original SMOTE oversampling algorithm, generating more representative data for underrepresented classes. We then use an RF optimization algorithm based on the correlation measure to identify loose particle signals in balanced data. Our experimental results demonstrate that the LR-SMOTE algorithm has a better data

Lv, Bingze, Wang, Guotao, Li, Shuo, Wang, Shicheng, Liang, Xiaowen

High-Voltage DC Electromechanical, Solid State, and Hybrid Switching Devices in Aerospace Applications

AIR7058 (Current)

05/10/2023

The scope of this SAE Aerospace Information Report (AIR) is first to establish applicable definitions and terms prior to considering the application domain and use cases in HVDC applications. Then it will identify commanded switching technologies to be considered for aerospace applications and provide rationale for their selection

AE-10 High Voltage Committee

Role of Worst-Case Operating Scenario and Component Tolerance in Robust Automotive Electronic Control Module Design

2023-01-0849

04/11/2023

Use of electronic systems in the vehicles is increasing day by day. As Electronic Control Modules (ECMs) become a large part of the vehicle, automotive designers need to take diligent decision of selecting electrical and electronic components. Selecting these components for ECM depends on four major factors: meeting stringent vehicle requirements, performance over the lifespan, robustness/reliability and cost. There is always an urge of reducing the cost of the ECM, but robustness of the controller module must not be compromised. One electrical or electronic component failure or false fault detection not only increases warranty cost but may also stall the vehicle, and interrupts customer’s daily routine creating dissatisfaction. This paper emphasizes on the importance of understanding worst-case operating scenarios considering component tolerances over the operating range, datasheet, and impact of tolerances on performance and fault detection. Wide ranges in component tolerances over

Hasan, S.M. Nayeem, Irgens, Peter, Murphy, Thomas

Cross-Domain Fault Diagnosis of Powertrain System using Sparse Representation

2023-01-0420

04/11/2023

Although excellent progress has been made recently in powertrain fault diagnosis based on vibration signals, most of them are based on the assumption that the fault features of the training and test data are drawn from the same probability distribution. Due to the limitation of the domain shift phenomenon, the performance of the current intelligent fault diagnosis methods is significantly reduced. Even many existing transfer learning methods have the problem of low generalization ability. Inspired by sparse representation theory, a novel cross-domain fault diagnosis method based on K-means singular value decomposition (K-SVD) and long short-term memory network (LSTM) is proposed in this study. First, K-SVD can convert source domain data into a sparse dictionary and sparse coefficient. The domain-invariant features are explored in the sparse dictionary, which contains redundant features. The sparse coefficients are input into the LSTM to obtain a primary classifier. Then, the sparse

Shen, Pengfei, Bi, Fengrong, Tang, Daijie, Yang, Xiao, Huang, Meng, Guo, Mingzhi, Bi, Xiaoyang

ARC Fault Circuit Breaker (AFCB), Aircraft, Trip-Free 28 VDC

AS6019 (Current)

03/27/2023

The primary function of this specification is to cover the general requirements for 28 VDC manual reset trip-free arc fault/thermal circuit breakers for use in aircraft electrical systems conforming to MIL-STD-704. As a secondary function, this specification may possibly cover the general requirements for AFCBs for use in primary vehicles, other than aircraft, when mounted directly to the structure

AE-7P Protective and Control Devices

ARC FAULT CIRCUIT BREAKER - AIRCRAFT, TRIP FREE, 1 TO 20 AMP, TYPE I

AS5692/3 (Current)03/27/2023

AE-7P Protective and Control Devices

ARC FAULT CIRCUIT BREAKER - AIRCRAFT, TRIP FREE, 25 TO 35 AMP, TYPE 1

AS5692/4 (Current)03/27/2023

AE-7P Protective and Control Devices

ARC Fault Circuit Breaker ( AFCB), Aircraft, Trip-Free Single Phase and Three Phase 115 VAC, 400 Hz - Constant Frequency

AS5692A (Current)

03/27/2023

The primary function of this specification is to cover the general requirements for manual reset trip-free arc fault/thermal circuit breakers for use in aircraft electrical systems conforming to MIL-STD-704. As a secondary function, this specification may possibly cover the general requirements for AFCBs for use in primary vehicles, other than aircraft, when mounted directly to the structure

AE-7P Protective and Control Devices

Piston Slap Condition Monitoring and Fault Diagnosis Using Machine Learning Approach

03-16-07-0051

03/11/2023

Various internal combustion (IC) engine condition monitoring techniques exist for early fault detection and diagnosis to ensure smooth operation, increased durability, low emissions, and prevent breakdowns. A fault, such as piston slap, can damage critical components like the piston, piston rings, and cylinder liner and is among those faults that may lead to such consequences. This research has been conducted to monitor piston slap conditions by analyzing the engine vibration and acoustic emission (AE) signals. An experimental setup has been established for acquiring vibration and AE sensor signatures for various piston slap severity conditions. Time-domain features are extracted from vibration and AE sensor signatures, and among them, the best features are selected using one-way analysis of variance (ANOVA) to create machine learning (ML) models. Apart from individual sensor feature classification, the feature fusion method increases the prediction accuracy. ML algorithms used in this

Kochukrishnan, Praveen, Rameshkumar, K., Srihari, S.

Enhancing Fault Isolation and Detection for Electric Powertrains of UAVs

TBMG-47338

01/01/2023

NASA’s System-Wide Safety (SWS) project is developing innovative data solutions to assure safe, rapid, and repeatable access to a transformed National Airspace System. The increasing number of electric propulsion systems that will enter the airspace will require systems that ensure high safety standards in the low-altitude airspace. One element that can help ensure safety is proper diagnosis of failures via Fault Detection and Isolation (FDI). NASA Ames has developed a fault isolation approach for electric powertrains of unmanned aerial vehicles

Effect of k-Nearest Neighbours (k-NN) in Classifying Planetary Gearbox Faults Using Statistical Features

2022-28-0556

12/23/2022

The use of planetary gearboxes in heavy-duty industries is dominant due to their compact size, large transmission ratio and torque delivery capability with different configurations. Due to their harsh operating conditions, localised gear tooth faults such as cracking and chipping are more common in such gearboxes. Furthermore, localised gear tooth failure initiates distributed gear faults such as pitting and wear on the gear tooth. Therefore, it is necessary to monitor such localised gear faults continuously and detect them at an early stage to prevent sudden and catastrophic failure. In this study, gear tooth localised defects on various gear elements of the planetary gearbox are seeded using Electrical Discharge Machine (EDM). Then the vibration signals from the gearbox are captured. Afterwards, a decision tree algorithm selects the most prominent statistical features from many extracted features. Further, to automate the fault detection process, the k-nearest neighbours (k-NN

Syed, Shaul Hameed, V, Muralidharan, D, Pradeep Kumar, S PhD, Ravikumar

Detection and Diagnosis of Speed Sensor Air Gap Change Fault

2022-01-705810/28/2022

Aiming at the fault that the speed signal of the automatic transmission output shaft Hall-type speed sensor fluctuates abnormally due to the change of the air gap, the method of fault detection and diagnosis is proposed. Firstly, a limited low-pass filter module was designed according to the characteristics of the fault, and a good filter effect is achieved. Secondly, by comparing the signals before and after filtering, a residual generator is designed, and an adaptive dynamic threshold is designed by analyzing the causes and influencing factors of the residual, which can configure a reliable and effective threshold for the generated residual in real time, which improves the fault identification robustness and effectiveness. Then, a fault debounce method is designed to avoid frequent false alarms of occasional faults. Finally, simulation verification proves the effectiveness of the method

Wang, Shuhan, Zheng, Wei, Yao, Kun, Dong, Peng, Xu, Xiangyang, Dai, Zhenkun, Huang, Yajun, Zhang, Xiujuan, Liu, Yi, Wang, Kaifeng, Mao, Feihong

Model-Based Multi-Fault Diagnosis for Lithium-Ion Battery Systems

2022-01-7034

10/28/2022

Accurate fault diagnosis is critical to the safe and efficient operation of lithium-ion battery systems. However, various faults in battery systems are difficult to detect and isolate due to their similar features. This paper proposes a model-based multi-fault diagnosis method to detect and isolate the current, voltage, and temperature sensor faults, short circuit faults, and connection faults in the lithium-ion battery systems. An electro-thermal model with fault information is established and used to construct the structural model. Structural analysis theory is applied to design diagnostic tests sensitive to multiple faults. To improve the accuracy and robustness of residual generation, the adaptive extended Kalman filter is introduced to battery state estimation. The multi-fault detection and isolation are implemented using residual evaluation based on the cumulative sum algorithm. Furthermore, a fault indicator used to distinguish short circuit and connection faults is presented

Zhang, Kai, Hu, Xiaosong, Deng, Zhongwei, Lin, Xianke

Challenges to develop a range monitor for all-electric vehicles

1332610/07/2022

Considering the rapid market development of all-electric vehicles, on-board diagnostics is facing new challenges. Up to now, OBD has focused on emission-relevant system functionalities. This study deals with the further development for battery electric vehicles, focusing on the detection of range impacts and the related cause classification (pinpointing). As there are currently no technical regulations regarding range monitoring, technical challenges as well as possible solution concepts are discussed. In the conceptual design and development of the range monitor, three focal points were defined: which components lead to a range impact, which framework conditions affects the error detection and what is a possible solution for this error. The challenges of the energy management of a battery electric vehicle lie in the multitude of operating states and the complexity of the required thermal management for passenger cell, battery temperature control and individual system components

Di, Gianni

Software Reliability Modelling and Application in Software Development Life Cycle

2022-28-0123

10/05/2022

With the accelerating demands of new features in embedded software viz diagnostic services, infotainment instigate complex software development. Ever-increasing software complexity gives rise to unreliable behaviours in the vehicle system. Software reliability model reinforces the confidence of the end-user about the compliant operation of the provided software with respect to requirements. This paper describes the application of software reliability engineering in the Software development life cycle. Further, we are demonstrating means to compute the software operational reliability by acquiring defects observed at the software testing phase. A detailed software reliability model selection process led us to conclude to a software reliability model based on the Nonhomogeneous Poisson process (NHPP) by Schneidewind. The discussed Software reliability model considers both fault detection and correction process for modelling and uses historical defect data of the software for the

Satpute, Apoorv Mohan, Priya, Jyoti, Mishra, Jitendra, Anilkumar, Sandhya

Anomaly Detection for Unmanned Aerial Vehicle Sensor Data Using a Stacked Recurrent Autoencoder Method with Dynamic Thresholding

01-15-02-0017

09/15/2022

With substantial recent developments in aviation technologies, unmanned aerial vehicles (UAVs) are becoming increasingly integrated in commercial and military operations internationally. Research on the applications of aircraft data is essential in improving safety, reducing operational costs, and developing the next frontier of aerial technology. Having an outlier detection system that can accurately identify anomalous behavior in aircraft is crucial for these reasons. This article proposes a system incorporating a long short-term memory (LSTM) deep learning autoencoder-based method with a novel dynamic thresholding algorithm and weighted loss function for anomaly detection of a UAV dataset, in order to contribute to the ongoing efforts that leverage innovations in machine learning and data analysis within the aviation industry. The dynamic thresholding and weighted loss functions showed promising improvements to the standard static thresholding method, both in accuracy-related

Bell, Victoria, Moral Arce, Ignacio, Mase, Jimiama M., Rengasamy, Divish, Rothwell, Benjamin, Figueredo, Grazziela P.

A Reduced-Order Modeling Framework for Simulating Signatures of Faults in a Bladed Disk

01-16-01-0006

08/29/2022

This article reports a reduced-order modeling framework of bladed disks on a rotating shaft to simulate the vibration signature of faults in different components, aiming toward simulated data-driven machine learning. We have employed lumped and one-dimensional analytical models of the subcomponents for better insight into the complex dynamic response. The framework addresses some of the challenges encountered in analyzing and optimizing fault detection and identification schemes for health monitoring of aeroengines and other rotating machinery. We model the bladed disks and shafts by combining lumped elements and one-dimensional finite elements, leading to a coupled system. The simulation results are in good agreement with previously published data. We model and analyze the cracks in a blade with their effective reduced stiffness approximation. Different types of faults are modeled, including cracks in the blades of a single- and two-stage bladed disks, Fan Blade Off (FBO), and Foreign

Singh, Divya Shyam, Agrawal, Atul, Roy Mahapatra, Debiprosad

Supervised Learning Classification Applications in Fault Detection and Diagnosis: An Overview of Implementations in Unmanned Aerial Systems

01-16-01-0004

08/18/2022

Statistical machine learning classification methods have been widely used in the fault detection analysis in several engineering domains. This motivates us to provide in this article an overview on the application of these methods in the fault diagnosis strategies and also their successful use in unmanned aerial vehicles (UAVs) systems. Different existing aspects including the implementation conditions, offline design, and online computation algorithms as well as computation complexity and detection time are discussed in detail. Evaluation and validation of these aspects have been ensured by a simple demonstration of the basic classification methods and neural network techniques in solving the fault detection and diagnosis problem of the propulsion system failure of a multirotor UAV. A testing platform of an Hexarotor UAV is completely realized. Measurements data issued from the onboard sensors are collected and a classification model to detect damaged propellers and failed motors has

Saied, Majd, Attieh, Hadi, Mazeh, Hussein, Shraim, Hassan, Francis, Clovis

Protecting Electric Vehicle Charging Systems with Advanced Current Sensing

TBMG-45970

06/01/2022

While battery range and charging times are getting the most attention when it comes to electric vehicle (EV) charging systems, safety and reliability are a critical part of the equation. Using the right current-sensing methodology can go far to address these concerns

A Mathematical Model of a UAV Spark-Ignition Engine for the Design of Control Methods

2022-01-5033

05/09/2022

This paper presents a real-time, nonlinear, control-oriented model for a two-stroke, spark-ignition aircraft engine. The safety and reliability of unmanned aerial vehicles (UAVs) are vital for their large-scale usage. Therefore, the design of control systems for normal as well as abnormal operation of UAVs is very essential. Timely detection and isolation of faults in an engine can save the aircraft from catastrophic consequences. Modeling is the first stage in the majority of control methods. This model is designed to be able to accurately and in real-time predict the output of an aircraft engine. Using existing modeling knowledge, a mean-value engine model is developed in this paper. The engine model consists of five submodels named the throttle body model, air dynamics model, fuel dynamics model, rotational dynamics model, and atmospheric model. The first four submodels are responsible for an accurate description of engine dynamics, while the atmospheric model covers the variation

Amin, Maliha, Kazmi, Ijaz Hussain, Khan, Abdul Qayyum

Fault Diagnosis and Prediction in Automotive Systems with Real-Time Data Using Machine Learning

2022-01-0217

03/29/2022

In the automotive industry, a Malfunction Indicator Light (MIL) is commonly employed to signify a failure or error in a vehicle system. To identify the root cause that has triggered a particular fault, a technician or engineer will typically run diagnostic tests and analyses. This type of analysis can take a significant amount of time and resources at the cost of customer satisfaction and perceived quality. Predicting an impending error allows for preventative measures or actions which might mitigate the effects of the error. Modern vehicles generate data in the form of sensor readings accessible through the vehicle’s Controller Area Network (CAN). Such data is generally too extensive to aid in analysis and decision making unless machine learning-based methods are used. This paper proposes a method utilizing a recurrent neural network (RNN) to predict an impending fault before it occurs through the use of CAN data. Methods to pre-process the vehicle data for dimensionality reduction

Hulbert, Scott, Mollan, Calahan, Pandey, Vijitashwa

Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means

2022-01-0616

03/29/2022

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels. The

Tang, Daijie, Bi, Fengrong, Yang, Xiao, Li, Xin, Shen, Pengfei, Tian, Congfeng

An Efficient Machine Learning Algorithm for Valve Fault Detection

2022-01-0163

03/29/2022

Multi-level Miller-cycle Dynamic Skip Fire (mDSF) is a combustion engine technology that improves fuel efficiency by deciding on each cylinder-event whether to skip (deactivate) the cylinder, fire with low (Miller) charge, or fire with a high (Power) charge. In an engine with two intake and two exhaust valves per cylinder, skipping can be accomplished by deactivating all valves, while firing with a reduced charge is accomplished by deactivating one of the intake valves. This new ability to modulate the charge level introduces new failure modes. The first is a failure to reactivate the single, high-charge intake valve, which results in a desired High Fire having the air intake of a Low Fire. The second is a failure to deactivate the single intake valve, which results in a Low Fire having the air intake of a High Fire. Reliably detecting these two faults has proven challenging for classical techniques that se measured MAP (Manifold Absolute Pressure) and/or crank angle acceleration to

Serrano, Joe, Ortiz-Soto, Elliott, Chen, S Kevin, Chien, Li-Chun, Joshi, Abhishek

Data-Driven Multi-Type and Multi-Level Fault Diagnosis of Proton Exchange Membrane Fuel Cell Systems Using Artificial Intelligence Algorithms

2022-01-0693

03/29/2022

To improve the durability of Proton-exchange membrane fuel cell (PEMFC) in actual transportation application scenario, the research on fault diagnosis of PEMFC is receiving extensive attention. With the development of artificial intelligence, performing fault diagnosis with the massive sampling data of the fuel cell system has become a popular research topic. But few people have successfully verified the diagnosis performance of these artificial intelligence algorithms on a real high power on-board PEMFC system. Therefore, we intend to make a step forward with these data-driven artificial intelligence algorithms. We applied four data-driven artificial intelligence algorithms to diagnose three common faults of PEMFC (each fault type has two severity levels, slight and severe). AVL CRUISE M was firstly applied for generation of simulation fault dataset to speed up the algorithm screening process. Based on the dataset, these algorithms are trained and optimized. The trained artificial

Zhou, Su, Wang, Keyong, Shan, Jing, Bao, Datong, Hou, Zhongjun, Yanda, Lu

Approach for Digitalization of Maintenance Processes within the Aircraft Cabin

2022-01-0021

03/08/2022

Enhancing the comfort for passengers, airlines are constantly increasing the number of services within the aircraft cabin such as meal ordering directly from passenger seats. The payment and menu selection can be completely processed by means of a passenger-individual airline user account also considering the remaining inventory of the galley. The implementation of such type of services is supported by digitalization of cabin business processes. For these new services the airline requires a high availability of the process-related system functions to ensure airline’s revenue and customer satisfaction. A possible approach to reach the target of high availability of these functions is to use the trend of digitalization for improving function-relevant maintenance processes within the aircraft cabin. This requires an introduction of flexible communication architectures to enhance the existing maintenance process by establishing an automated fault detection, root cause analysis, and/or even

Hintze, Hartmut, Giertzsch, Fabian, Kusch, Alexander, God, Ralf

Rolling bearing fault identification using BAT algorithm for the resonant demodulation parameters optimization

2021-36-0084

02/04/2022

The rolling bearing is a fundamental component of rotating machines and its failure may lead to a catastrophic damage of the system. The incipient and correct identification of faults contribute to an early predictive maintenance plan, which avoids additional costs and sudden breakdowns. The resonant demodulation technique, envelope analysis, is a well-established method widely used to identify failures in rolling bearings. However, this method requires the identification of the frequency region that contains enough information about the faults. Thus, the spectral kurtosis gives the impulsiveness measure of a vibration signal and it is used to identify the frequency region of failure. This paper presents the use of the bat algorithm as an optimization methodology to identify the resonant demodulation parameters using spectral kurtosis as the objective function. Bat algorithm is a recent method based on echolocation behavior becoming a powerful option in face of traditional method as

Paes, Joed Henrique, de Freitas, Thiago Caetano, Gioria, Gustavo Dos Santos

653P3A-2 Avionics Application Software Standard Interface, Part 3A, Conformity Test Specifications for ARINC 653 Required Services

ARINC653P3A-2 (Current)11/17/2021

ARINC 653, Part 3A is the Compliance Test Specification for ARINC 653 Required Services presently defined in ARINC 653 Part 1. The document specifies a set of stimuli and the expected responses. Future work on the ARINC 653 document set includes an effort to define Operating System services for multi-core processor environments. The Compliance Test Specification is expected to be updated in step with ARINC 653, Part 1

Airlines Electronic Engineering Committee

Fault Detection and Diagnosis of Engine Spark Plugs Using Deep Learning Techniques

03-15-04-0027

11/10/2021

Fault Detection and Diagnosis (FDD) is playing an increasingly important role in the automotive sector as it moves toward Advanced Technology Vehicles. Reducing the cost of sensory equipment to detect faults in Internal Combustion Engines (ICEs) has always been a common desire for automotive researchers. This article offers an Artificial Intelligence approach for detecting engine combustion faults related to spark plugs using existing sensors. The study investigates two deep learning models that are capable of learning different fault conditions from historical sensory data. The two customized models, one Long Short-Term Memory (LSTM) neural network and one Convolutional Neural Networks (CNN) model, are proposed to tackle this task. The LSTM model processes the filtered sensor data in time series, while the CNN model uses the frequency map that is novel in the learning-based engine diagnosis field. A comprehensive engine fault dataset is collected and includes a variety of operating

Huangfu, Yixin, Seddik, Essam, Habibi, Saeid, Wassyng, Alan, Tjong, Jimi

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