Browse Topic: Statistical analysis
Vehicle interior noise is a crucial assessment criterion for automotive NVH. It has a significant effect on customer opinions about the quality of a vehicle. Articulation Index (AI) is one of the key sound metrics used to describe speech intelligibility and quantifies the middle and high frequency spectra associated to the internal noise of vehicle. In reality, Vehicle operating under dynamic condition experiences various air-borne noise sources such as tire rolling noise, powertrain noise, intake-exhaust noise & wind noise along with structure borne excitations such as powertrain vibrations, suspension vibrations. It is very challenging to predict cumulative effect of all these excitations to interior noise level and Articulation Index (AI) of vehicle over complete frequency range. The statistical energy analysis (SEA) is a well-known methodology being used to simulate & predict mid & high frequency noise. Objective of this paper is to present the process of development of a SEA
In vehicle development, occupant-centered design is crucial to ensuring customer satisfaction. Key factors such as visibility, access, interior roominess, driver ergonomics, interior storage and trunk space directly impact the daily experience of vehicle occupants. While automakers rely on engineering metrics to guide architectural decisions, however in some cases doesn’t exist a clear correlation between these quantitative parameters and the subjective satisfaction of end users. This study develops a methodology which addresses that gap by proposing the creation of quantitative satisfaction curves for critical engineering metrics, providing a robust tool to support decision-making during the early stages of vehicle design. Through a combination of clinics, research, and statistical analysis, this project outlines a step-by-step process for developing (dis)satisfaction curves, offering a clearer understanding of how dimensions like headroom, glove box volume, and A-pillar obscuration
Accurate defect quantification is crucial for ensuring the serviceability of aircraft engine parts. Traditional inspection methods, such as profile projectors and replicating compounds, suffer from inconsistencies, operator dependency, and ergonomic challenges. To address these limitations, the 4D InSpec® handheld 3D scanner was introduced as an advanced solution for defect measurement and analysis. This article evaluates the effectiveness of the 4D InSpec scanner through multiple statistical methods, including Gage Repeatability and Reproducibility (Gage R&R), Isoplot®, Youden plots, and Bland–Altman plots. A new concept of Probability of accurate Measurement (PoaM)© was introduced to capture the accuracy of the defect quantification based on their size. The results demonstrate a significant reduction in measurement variability, with Gage R&R improving from 39.9% (profile projector) to 8.5% (3D scanner), thus meeting the AS13100 Aerospace Quality Standard. Additionally, the 4D InSpec
In-Use emission compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding emission compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for emission compliance risk assessment using Fleet Intelligence Data, which includes high-frequency telematics data from over 500K machines, reporting more than 1000 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for emission compliance assessment. The resulting reports provide actionable
In today’s competitive landscape, industries are relying heavily on the use of warranty data analytics techniques to manage and improve warranty performance. Warranty analytics is important since it provides valuable insights into product quality and reliability. It must be noted here that by systematically looking into warranty claims and related information, industries can identify patterns and trends that indicate potential issues with the products. This analysis helps in early detection of defects, enabling timely corrective actions that improve product performance and customer satisfaction. This paper introduces a comprehensive framework that combines conventional methods with advanced machine learning techniques to provide a multifaceted perspective on warranty data. The methodology leverages historical warranty claims and product usage data to predict failure patterns & identify root causes. By integrating these diverse methods, the framework offers a more accurate and holistic
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
A good Noise, Vibration, and Harshness (NVH) environment in a vehicle plays an important role in attracting a large customer base in the automotive market. Hence, NVH has been given significant priority while considering automotive design. NVH performance is monitored using simulations early during the design phase and testing in later prototype stages in the automotive industry. Meeting NVH performance targets possesses a greater risk related to design modifications in addition to the cost and time associated with the development process. Hence, a more enhanced and matured design process involves Design Point Analysis (DPA), which is essentially a decision-making process in which analytical tools derived from basic sciences, mathematics, statistics, and engineering fundamentals are used to develop a product model that better fulfills the predefined requirement. This paper shows the systematic approach of conducting a Design Point Analysis-level NVH study to evaluate the acoustic
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