Electric vehicles (EVs) are paving the way for future mobility, with drive motors playing a central role in their efficiency and performance. Motor testing machines are crucial for validating EV motors, yet flaws in testing equipment, such as gear issues, often lead to operational disruptions. This study aims to enhance motor testing by implementing machine learning and vibration signal analysis to detect gear faults early. Using statistical feature extraction and classifiers like Quadratic SVM and Bagged Trees, the collected vibration signals are categorized as normal or faulty under loaded (0.275 kW) and no-load conditions. Performance comparison reveals the Bagged Trees algorithm's superior accuracy of 95.3%. This approach offers an intelligent, preventive maintenance solution, improving the motor test bench’s reliability.

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

Fault Diagnosis of PEMFC Based on Fast EIS Measurement and Optimized Random Forest Algorithm

2025-01-7080

01/31/2025

Electrochemical impedance spectroscopy (EIS) is often used for fault diagnosis as an important parameter to characterize the state of fuel cells. However, online diagnosis requires high real-time performance and usually can only measure single-frequency or dual-frequency impedance. Too few diagnostic features make it difficult for traditional fault diagnosis methods based on EIS to ensure high accuracy. Therefore, this paper proposes a fault diagnosis method based on fast EIS measurement and an optimized random forest algorithm. Firstly, using a multi-sine excitation signal to realize the simultaneous measurement of multi-frequency impedance, provides more health status information in a single measurement. To solve the problem of large signal peaks caused by the superimposed signals, the phase is optimized by the genetic algorithm, which reduces the crest factor of the excitation signal. Then, multi-frequency impedance is used as a training feature for the random forest (RF) algorithm

Ni, Shengqi, Zhang, Cunman, Zhu, Yuan, Zhong, Xiaolong

Data Generation and Preliminary Fault Detection of Power Battery Based on GAN Algorithm

2025-01-7009

01/31/2025

The safety of power batteries is an important issue that has attracted widespread attention in new energy vehicle technology. In this paper, Generative Adversarial Networks (GAN) are introduced, and the data generation and fault diagnosis of power battery life-cycle data are carried out. GAN is composed of a pair of generators and discriminators, combining signal processing with neural networks, using the discriminator architecture based on Fourier transform and the generator architecture based on wavelet transform, so that the neural network can learn the characteristics of power battery life-cycle data from the perspective of time and frequency domain, and use the good performance of wavelet transform in data denoising and repair to generate high-quality and low-noise data, and use Fourier transform to target the characteristics of periodicity. Identify and distinguish the periodic characteristics and time-frequency domain data characteristics in the generated data and laboratory

Tan, Piqiang, Yang, Aoji, Liu, Xiang, Yao, Chaojie

Multi-step Prediction of Battery Temperature and Thermal Fault Diagnosis Based on Informer

2025-01-7019

01/31/2025

Lithium-ion batteries are prone to thermal failures under extreme conditions, leading to thermal runaway and safety risks such as fire or explosion. Therefore, effective temperature prediction and diagnosis are crucial. This paper proposes a thermal fault diagnosis method based on the Informer time series model. By extracting temperature-related features and conducting correlation analysis, a 9-dimensional input parameter matrix is constructed. Experimental results show that the model can maintain an absolute temperature prediction error within 0.5°C when predicting 10 seconds in advance, with higher accuracy than the LSTM model. Additionally, a three-level warning mechanism based on the forgetting coefficient further enhances diagnostic accuracy. Validation using test data and real vehicle data demonstrates that this method can efficiently diagnose and locate thermal faults in batteries, with low computational costs, making it suitable for online applications.

Sun, Yefan, Zhu, Xiaopeng, Zhang, Zhengjie, Peng, Zhaoxia, Yang, Shichun, Liu, Xinhua

Research on Weak Fault Diagnosis Method of Dual - Axes Precision Gear Transmission System

2025-01-7052

01/31/2025

This paper focuses on the weak fault diagnosis of a dual - axes precision gear transmission system. Firstly, it elaborates on the structure and working principle of the system. Comprising components like azimuth and pitch channels, motors, and control units, the pitch channel's gear transmission chain is a key research area. Subsequently, fault modes and their harmfulness are analyzed. Different faults such as tooth surface wear and pitting are considered. These faults can lead to serious consequences like system failure and mission deviation. Based on this, a test system is constructed. It includes sensors and a data acquisition system to simulate faults and collect vibration signals. The signals are then analyzed to understand the system's behavior. Finally, a weak fault feature index based on time - domain entropy is developed. A threshold setting method based on severity index is also proposed. These methods together enable the accurate diagnosis of weak faults in the system, which

Han, Wei, Chang, Yingjie

Safety Analysis of Traction Batteries Based on Micro Internal Short Circuit Damage

2025-01-7017

01/31/2025

The internal short circuit of a traction battery is one of the most typical failure mechanisms that can lead to thermal runaway, potentially triggering thermal propagation across the entire battery system. This phenomenon poses significant safety risks, especially in electric vehicles and large-scale energy storage systems. Therefore, it is essential to explore and understand the internal short circuit behavior to mitigate these risks. One of the most effective testing methods for reproducing an internal short circuit is the penetration test, where specific test conditions must be carefully designed based on the failure behavior. Among these conditions, the penetration step length plays a crucial role, as it directly influences the short circuit dynamics. Despite the importance of penetration step length, there is currently no standardized test procedure that dictates how to select the appropriate step size for different battery samples. This gap in standardization complicates the

Wang, Fang, Sun, Zhipeng, Ma, Tianyi, Dai, Xiaoqian, Dai, Ce, Yan, Pengfei, Ma, Xiaole, Chen, Liduo, Ma, Haishuo, Shen, Shaopeng

Fault Diagnosis of Proton Exchange Membrane Fuel Cells Based on Deep Learning and Transfer Learning

2025-01-7076

01/31/2025

To accurately identify the fault types of proton exchange membrane fuel cell (PEMFC) systems under continuously varying operating currents, this study develops a comprehensive PEMFC system model and proposes a robust fault diagnosis method based on the ResNet50 convolutional neural network (CNN) and transfer learning (TL). Initially, using Matlab/Simulink, a PEMFC model is constructed based on the electrochemical reaction mechanisms and empirical formulas that characterize the operation of the fuel cell. This model primarily includes the fuel cell stack and various auxiliary systems, such as the thermal management system, air supply system, and hydrogen supply system, each crucial for optimal performance. By varying the model parameters, sensor data is generated for five distinct operating conditions. After preprocessing the data, the Gramian Angular Field (GAF) technique is utilized to convert the time series data from each sensor into fault data images, which then serve as input for

Zhu, Shaopeng, Wang, Yifeng, Xiong, Qinghui, Geng, Jun, Chen, Huipeng

Fault Diagnosis Strategy for Proton Exchange Membrane Fuel Cells Based on PSO-BP Neural Network Using Particle Swarm Optimization Algorithm

2025-01-7085

01/31/2025

This paper presents a fault diagnosis strategy that integrates model-based and data-driven approaches for a 115 kW proton exchange membrane fuel cell used in vehicles. First, a stack subsystem model was developed in the MATLAB/Simulink platform based on the working principles and structure of PEMFC, and validated with experimental data. Subsequently, faults in the air and hydrogen inlet pipelines were simulated, and the resulting fault data were subjected to preprocessing steps, including cleaning, normalization, and feature extraction, to enhance the efficiency of subsequent data processing. Finally, a BP neural network optimized by particle swarm optimization was employed to achieve fault tree-based classification diagnosis. Experimental results indicate that the diagnosis accuracy of the BP neural network reached 96.04%, with an additional accuracy improvement of approximately 2.4% after PSO optimization.

Wang, Ze, Zhu, Shaopeng, Chen, Ping, Li, Congxin, Zhou, Wenhua

Fault Detection of Diesel Engines Using Artificial Neural Network

03-18-02-0010

01/20/2025

This study presents a method for identifying the reliability state of diesel engines by utilizing artificial neural networks (ANNs). The Sulzer 6AL20/24 marine diesel engine was selected as the test subject for this research. Vibration signals were collected during tests conducted on a laboratory test stand under normal operating conditions and during simulations of six different engine faults. Next, the recorded signals were analyzed and transformed into labeled samples for supervised learning. In this phase, the time histories of the vibration signals were divided into segments and augmented, with several key features calculated for each segment. Highly correlated signals were excluded from further analysis based on the Pearson correlation coefficient. The processed samples were then used to train and fine-tune the ANN. The trained ANN was subsequently used to identify the engine’s reliability state and classify the present fault type. To evaluate the effectiveness of the proposed

Pająk, Michał, Kluczyk, Marcin, Muślewski, Łukasz, Lisjak, Dragutin

Development and Simulation of an AUTOSAR Software Component for Functional Safety according to ISO 26262 Fault Detection and Safety Mechanisms

2024-36-0132

12/20/2024

In the context of advancing automotive electronic systems, ensuring functional safety as per ISO 26262 standards has become of primary importance. This paper presents the development of an AUTOSAR-compliant Software Component (SWC) applied to ISO 26262 applications. Using MATLAB/Simulink, we design and simulate a SWC that operates within the AUTOSAR architecture, focusing on fault detection and activation of safety mechanisms. The SWC is built to monitor specific system parameters and operational anomalies. Upon detecting a fault, it triggers predefined safety mechanisms to mitigate risks and ensure system integrity. The simulation focus on capability to accurately identify faults and execute safety measures effectively, thus demonstrating a practical approach to enhance automotive system safety implementation and its reuse. This paper not only highlights the importance of ISO 26262 in the automotive industry but also illustrates the feasibility of developing and integrating safety

Santiago, Frederico Victor Scoralick, dos Santos Machado, Cleberson, Imbasciati, Henrique, Costa, Silvio Romero Alves

Digital Twin-Enabled Fault Detection for Suspension Systems in Autonomous Mining Haulage Vehicles

2024-36-0170

12/20/2024

Autonomous vehicles for mining operations offer increased productivity, reduced total cost of ownership, decreased maintenance costs, improved reliability, and reduced operator exposure to harsh mining environments. A large flow of data exists between the remote operation and the ore haul vehicle, and part of the data becomes information for the maintenance sector which it monitors the operating conditions of various systems. One of the systems deserving attention is the suspension system, responsible for keeping the vehicle running and within a certain vibration condition to keep the asset operational and productive. Thus, this work aims to develop a digital twin-assisted system to evaluate the harmonic response of the vehicle’s body. Two representations were created based on equations of motion that modeled the oscillatory behavior of a mass-damper system. One of the representations indicates a quarter of the ore transport truck’s hydraulic system in a healthy state, called a virtual

Rosa, Leonardo Olimpio, Branco, César Tadeu Nasser Medeiros

Redundant Brake Boost Control Strategy of Integrated Electro-hydraulic Braking System

2024-01-7038

12/13/2024

The traditional braking system has been unable to meet the redundant safety requirements of the intelligent vehicle for the braking system. At the same time, under the change of electrification and intelligence, the braking system needs to have the functions of braking boost, braking energy recovery, braking redundancy and so on. Therefore, it is necessary to study the redundant braking boost control of the integrated electro-hydraulic braking system. Based on the brake boost failure problem of the integrated electro-hydraulic brake system, this paper proposes a redundant brake boost control strategy based on the Integrated Brake Control system plus the Redundant Brake Unit configuration, which mainly includes fault diagnosis of Integrated Brake Control brake boost failure, recognition of driver braking intention based on pedal force, pressure control strategy of Integrated Brake Control brake boost and pressure control strategy of Redundant Brake Unit brake boost. The designed control

Dexing, Lao, Luping, Yan, Qinghai, Sui, Long, Cao, Shang, Gao, Zhigang, Chen, Mingxing, Ren, Zhicheng, Chen

Safe Phase Current Measurement for Motor Control Applications

2024-28-0141

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

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

2024-28-0195

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

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

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

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

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

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

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

A Comparative Analysis of Fault Diagnosis by vibration signals for Critical Gear Components in Electric Vehicle Motor Testing Machines Using Machine Learning Algorithms

2025-01-004005/05/2023

Electric vehicles (EVs) represent the future of the automobile industry, with the drive motor being a crucial component in modern EVs. To ensure optimal performance, a motor testing machine is used to verify the essential parameters of these electrical motors. This machine regularly evaluates electric motor characteristic curves and their electromagnetic behavior under different conditions. However, it has been observed that flaws in the testing machine's helical gear arrangement frequently cause malfunctions and suboptimal performance, affecting the efficiency of the electric vehicle production line. This research project aims to improve the motor test bench apparatus by modifying critical design parameters using machine learning and vibration signal analysis methods. Vibration signals are recorded at various gear settings, and statistical features are extracted from these signals. These signals are then classified using classifiers such as Naive Bayes and Decision Trees. Machine

S PhD, Ravikumar, Sharik, N, Syed, Shaul, V, Muralidharan, D, Pradeep Kumar

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

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

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

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 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

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

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