Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance:A Review

Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle breakdowns.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.

Dynamics and Fault Diagnosis of Railway Vehicle Gearboxes:A Review

The railway vehicle gearbox is an important part of the railway vehicle traction transmission system which ensures the smooth running of railway vehicles.However,as the running speed of railway vehicles continues to increase,the railway vehicle gearbox is exposed to a more demanding operating environment.Under both internal and external excitations,the gearbox is prone to faults such as fatigue cracks,and broken teeth.It is crucial to detect these faults before they result in severe failures and accidents.Therefore,understanding the dynamics and fault diagnosis of railway vehicle gearbox is needed.At present,there is a lack of systematic review of railway vehicle gearbox dynamics and fault diagnosis.So,this paper systematically summarizes the research progress on railway vehicle gearbox dynamics and fault diagnosis.To this end,this paper first summarizes the latest research progress on the dynamics of railway vehicle gearboxes.The dynamics and vibration characteristics of the gearbox are summarized under internal and external excitations,as well as faulty conditions.Then,the stateof-the-art signal processing and artificial intelligence methods for fault diagnosis of railway vehicle gearboxes are reviewed.In the end,future research prospects are given.

Compound Fault Diagnosis for Rotating Machinery:State-of-the-Art,Challenges,and Opportunities

Compound fault,as a primary failure leading to unexpected downtime of rotating machinery,dramatically increases the difficulty in fault diagnosis.To deal with the difficulty encountered in implementing compound fault diagnosis(CFD),researchers and engineers from industry and academia have made numerous significant breakthroughs in recent years.Admittedly,many systematic surveys focused on fault diagnosis have been conducted by reputable researchers.Nevertheless,previous review articles paid more attention to fault diagnosis with several single or independent faults,resulting in that there is still lacking a comprehensive survey on CFD.Therefore,to fulfill the above requirements,it is necessary to provide an in-depth overview of fault diagnosis methods or algorithms for compound faults of rotating machinery and uncover potential challenges or opportunities that would guide and inspire readers to devote their efforts to promoting fault diagnosis technology more effective and practical.Specifically,the backgrounds,including the related definitions and a new taxonomy of CFD methods,are detailed according to the way of implementing compound fault recognition.Then,the stateof-the-art applications of CFD are overviewed based on relevant publications in the past decades.Finally,the challenges and opportunities associated with implementing CFD are concluded and followed by a conclusion for ending this survey.We believe that this review article can provide a systematic guideline of CFD from different aspects for potential readers and seasoned researchers.

Machinery fault diagnosis expert system based on case-based reasoning

A mechinery fault diagnosis expert system based on case-based reasoning (CBR) technology was established. The process of the CBR fault diagnosis is analyzed from three main aspects: expression and memory, retrieving and matching, and modification and maintenance of a case. The results indicate that the CBR method is flexible and simple to implement, and it has strong self-studying ability. Using a large enough number of case reasoning sets, it can accumulate the experience of problem solving, avoid the difficulty of knowledge acquisition, shorten the course of solving problems, improve efficiency of reasoning, and save the time of developing.

Analysing Recent Breakthroughs in Fault Diagnosis through Sensor:A Comprehensive Overview

Sensors,vital elements in data acquisition systems,play a crucial role in various industries.However,their exposure to harsh operating conditions makes them vulnerable to faults that can compromise system performance.Early fault detection is therefore critical for minimizing downtime and ensuring system reliability.This paper delves into the contemporary landscape of fault diagnosis techniques for sensors,offering valuable insights for researchers and academicians.The papers begin by exploring the different types and causes of sensor faults,followed by a discussion of the various fault diagnosis methods employed in industrial sectors.The advantages and limitations of these methods are carefully examined,paving the way for highlighting current challenges and outlining potential future research directions.This comprehensive review aims to provide a thorough understanding of current advancements in sensor fault diagnosis,enabling readers to stay abreast of the latest developments in this rapidly evolving field.By addressing the challenges and exploring promising research avenues,this paper seeks to contribute to the development of more robust and effective sensor fault diagnosis methods,ultimately improving the reliability and safety of industrial and agricultural systems.

Application of Machine Learning in Electronic Device Fault Diagnosis

As electronic devices become increasingly complex, traditional fault diagnosis methods face significant challenges. Machine learning technologies offer new opportunities and solutions for electronic device fault diagnosis. This paper explores the application of machine learning in electronic device fault diagnosis, focusing on common machine learning algorithms, data preprocessing techniques, and diagnostic model construction methods. Case study analysis elucidates the advantages of machine learning in improving diagnostic accuracy, reducing diagnosis time, and implementing predictive maintenance. Research indicates that machine learning techniques can effectively enhance the efficiency and precision of electronic device fault diagnosis, providing robust support for device reliability and maintenance strategy optimization. In the future, as artificial intelligence technology further develops, machine learning will play an increasingly important role in the field of electronic device fault diagnosis.

基于人工智能的机床主轴故障诊断研究

随着信息通信技术(ICT)的发展,人工智能技术在机械故障诊断中的应用引起了研究人员的关注。为了验证人工智能技术在机床主轴故障诊断方面的适用性,通过构建机床测试台,收集人为改变主轴偏心的故障数据,并采用3种人工智能模型(CNN、LSTM和AE)进行学习,分析比较了它们对主轴7种故障状态分类的准确性。实验结果表明:CNN和LSTM模型均具有较高的准确性,其中CNN模型的准确率最高,达到了99.3%,而AE模型的准确性相对较低,只有76.9%。验证了在机床主轴故障诊断中应用人工智能技术的可行性。

基于人工智能技术的雨量校准故障诊断与预警辅助系统研究

基于人工智能技术的雨量校准故障诊断与预警辅助系统,通过气象观测数据“云”获取设备计量数据进行预处理,采用多种数据驱动和人工智能算法,利用深度学习及神经网络,对采集的数据进行分析,对数据样本进行训练学习,诊断设备是否存在故障并对设备存在的风险进行预警判断。采用神经网络分析设备故障,根据分析出的设备故障情况,系统以大数据为核心、智能算法为底层逻辑模式分析并推送解决方案,有效地提升了户外计量工作效能,对气象自动站其他高精度传感器检定、校准的多源数据分析和诊断具有较好的开拓意义。

反激式开关电源故障非侵入式AI诊断方法研究

将人工智能技术应用到故障诊断领域可以实现电力设备的自动化、智能化诊断,提高诊断精度和效率。以单输入多输出的反激式开关电源为例,针对其因脆弱元件失效而引起的电路工作性能异常的问题,通过分析不同故障模式的信号特性和可分性,提出了融合输入电流和输出电压信息的非侵入式开关电源故障诊断方法。构建了由时域特征及频带小波包奇异熵特征组成的融合时频域信息的多维特征矢量,建立了故障特征与故障模式之间的映射关系。进而,提出了基于人工智能技术的深度神经网络(DNN)故障诊断方法,实时监测反激式开关电源的运行状态,并通过数据分析及时识别故障位置,对潜在故障进行预警。实验结果表明,所提出的方法对单故障和多故障模式均具有良好的诊断效果,诊断准确率可达97.9%,并且,在不同工况下,该方法均可表现出较高的诊断准确率和较强的抗干扰性能。

人工智能背景下机电一体化设备的故障诊断技术优化

随着工业的自动化与智能化,机电一体化设备复杂性提高,故障诊断难度增加。本文回顾机电一体化故障诊断技术的现状后,从数据处理、系统动态性、新型故障模式识别、整合实施进行分析,重点讨论了人工智能,尤其是机器学习和深度学习在优化故障诊断中的应用,包括数据驱动的诊断方法、预测性维护和算法的实施挑战。通过案例展示人工智能在实际故障诊断中的应用效果和价值。最后,展望该技术的发展,强调智能化和自动化的重要性。

人工智能在农机软件故障诊断和预测性维护中的应用研究

探讨了人工智能技术在农机软件故障诊断和预测性维护方面的应用。首先,分析了农机软件功能安全的重要性和现有挑战;然后,深入探讨了人工智能在数据处理、故障模式识别、实时监测、长期性能监测和用户行为分析等方面的应用潜力;最后,预测了未来发展趋势,并总结了人工智能技术在推动农业机械化方面的重要作用。

基于大语言模型的电力系统通用人工智能展望:理论与应用

大语言模型(LLM)是一种利用大规模文本语料库进行预训练和微调的深度学习语言模型。目前,在通识问答、文本生成和科学推理等方面已展现出强大的能力。在此背景下,文中探索了基于LLM构建面向电力系统的通用人工智能技术,并展望其在电力系统中的潜在应用。首先,介绍了LLM的基本原理、神经网络架构以及训练方法,特别是与传统人工智能模型相比,LLM在逻辑推理、编程和代码理解以及数学推理方面的突破。然后,展望了LLM在电力系统负荷与新能源发电出力预测、电力系统规划、电力系统运行、电力系统故障诊断与系统恢复、电力市场等领域的潜在应用。最后,阐述了基于LLM构建电力系统通用人工智能技术所面临的挑战,包括电力系统数据的质量与可获取性、输出结果可解释性以及隐私保护问题。

一种可解释人工智能(XAI)在测量设备故障诊断和寿命预测中的应用

基于人工智能算法的变压器故障诊断和寿命预测模型在提高准确率方面已经达到了很好的效果,但是仍存在泛化性能较低,对数据质量要求过高,判断结果无法解释等问题.该文基于DBSO-CatBoost模型,提出一种可用于故障判断解释的变压器故障诊断方法.该方法基于数据特征提取,采用差分变异头脑风暴优化(DBSD)算法对CatBoost模型进行优化和故障诊断.①对于数据预处理,引入比率法在原始数据中添加特征;采用基于可解释人工智能(XAI)的Shapley加法解释(SHAP)技术进行特征提取,并采用核主成分分析算法对数据进行降维.Shapley加法解释技术可根据特征贡献解码每个预测来帮助全局解释并评估预测结果.②将预处理后的数据输入到CatBoost模型中进行训练,并采用差分变异头脑风暴优化算法对CatBoost模型的参数进行优化,从而得到最优模型.③利用得到的优化模型诊断变压器故障并输出故障类型与预测结果.实验使用来自中国国家电网公司西北部某电网的真实数据评估该模型.结果表明:该文模型在不同故障诊断中的准确性最佳,平均准确率高达99.29%,证明该文方法可以有效提高电力变压器故障诊断的准确性和效率.

基于人工智能的供配电系统故障诊断与恢复策略

本文研究了基于人工智能的供配电系统的故障诊断与恢复策略。首先,对智能供配电系统的基本架构和原理进行深入探讨,包括网络感知、数据采集、实时监控和故障检测等方面,揭示了其核心组成部分的协同作用。其次,对机器学习在供配电系统故障诊断中的应用进行研究,阐述了机器学习在识别异常情况、分类故障类型和提供决策支持方面的关键作用,强调了其在优化系统性能和减少停电影响方面的巨大潜力。探讨了深度学习技术在供配电系统故障诊断中的应用,包括声音和图像分析,论述了如何利用深度学习技术处理大规模数据集,以提前发现潜在问题,从而更加全面地保障电力系统的稳定运行。最后,研究了恢复策略和自愈系统,探讨了如何在故障发生后自动触发恢复策略,以最小化停电时间和降低经济损失。强调了自动化切换设备和远程控制的重要性,以实现电力系统的快速、有效的自愈。

基于字词混用集成模型的电力变压器缺陷记录文本挖掘方法

变压器运维管理中积累了海量以文本形式记录的非结构化缺陷数据,但缺乏有效挖掘手段导致其利用率极低。文中提出一种基于字词混用集成模型的变压器缺陷记录文本挖掘方法,首先对变压器缺陷文本进行文本分词、去除停用词、文本增强、文本特征表示等预处理,以文本数学向量形式为输入,集成多个词汇级和字符级分类模型,通过元学习器对各基学习器性能的协同互补作用,实现变压器缺陷类型的准确识别和分类。与单一文本分类算法相比,该方法能够更全面地获得文本的语义特征,分类精确率达91%,模型准确率和召回率的综合评价分数F 1=0.9。将自然语言处理技术应用于电力设备缺陷记录文本,可以实现精准高效分类和故障识别,唤醒数据资源,显著提升电力变压器智能化管理水平。

基于人工智能的石化机组故障诊断检测算法

石化机组故障诊断对于现代工业系统的可靠性和安全性具有重要意义。人工智能(AI)技术作为工业应用的新兴领域和故障识别的有效解决方案,日益受到学术界和工业界的关注。然而,在不同的运行条件下,人工智能方法面临着巨大的挑战。从理论背景和工业应用两方面对石化机组故障诊断中的人工智能算法进行全面阐述。介绍不同的人工智能算法,包括K近邻、朴素贝叶斯、支持向量机、人工神经网络和深度学习等方法;对AI算法在工业应用中进行了广泛的文献调研;最后,对不同AI算法的优势、局限性、实践启示进行总结,表明了技术进步、多模态数据整合、实时监测预测、算法通用性对提升石化工业效率与可靠性的关键作用,并展望了未来的研究方向与挑战。

Intelligent fault diagnosis methods toward gas turbine: A review

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

变压器状态评估及故障诊断研究综述

电力变压器状态评估及故障诊断为设备安全稳定运行提供了重要保障。在电力大数据广泛应用的背景下,智能电网结构快速构建,电力设备状态数据呈现出数量大、类型多等特征,因而变压器状态评估及故障诊断算法由阈值判断法逐步过渡为机器学习等算法。本文作者总结了近年来国内外变压器监测研究中采用的方法;概述了变压器状态评估和故障诊断领域的研究现状,介绍了常用算法相关原理,包括模糊理论法、集对分析法、传统机器学习算法、预测算法和深度机器学习算法等;分析了目前该领域亟需解决的问题,并对未来研究方向进行了展望。

Interpretable data-driven fault diagnosis method for data centers with composite air conditioning system

Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.

电力设备故障智能识别技术研究

电力设备良好的运行状态是电力系统安全、可靠、经济运行的前提。利用人工智能技术算法、算力的优势能够大幅提升电力设备的故障诊断水平。本文以电力设备故障智能识别技术为研究主题,首先总结了电力设备运行故障数据的特性,并针对该数据存在的多源异构、不均衡性等特性,介绍了几种应用于电力设备故障诊断的新型人工智能技术,总结了人工智能技术在故障识别业务场景中的典型应用,最后,探讨了现阶段人工智能技术在实际应用中面临的挑战,并对未来的研究方向进行了展望。