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 break-downs.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.

Assessment on Fault Diagnosis and State Evaluation of New Power Grid:AReview

1 Introduction The proposal of the concept of“New Power System”aims to illustrate the transform direction of the traditional power system,acting as the development core of the future new power grid.To achieve this,the proposed strategic targets of“carbon neutralization and carbon peaking”must be implemented and insisted[1].The core feature of the new power system is that renewable energy plays a leading role and becomes the main source of energy supply,meanwhile,the goal of green energy utilization has also been put forward on the agenda.Green energy utilization includes two aspects,one is the exploitation and promotion of various green energy technologies,and the other is the digitalization of energy management.Under this trend,stochastic and fluctuating energy sources such as wind power and photovoltaic power replace deterministic controllable power sources such as thermal power,bringing challenges to power grid regulation and dispatching,as well as flexible operation.The large-scale integration of renewable energy and increasingly high proportion of power electronic equipment tend to bring about fundamental changes in the operation characteristics,safety control,and production mode of the power system.

Review of Condition-Based Maintenance Strategies for Offshore Wind Energy

The existing maintenance strategies of offshore wind energy are reviewed including the specific aspects of condition-based maintenance, focusing on three primary phases, namely, condition monitoring, fault diagnosis and prognosis, and maintenance optimization. Relevant academic research and industrial applications are identified and summarized. The state of art, capabilities,and constraints of condition-based maintenance are analyzed. The presented research demonstrates that the intelligent-based approach has become a promising solution for condition recognition, and an integrated data platform for offshore wind farms is significant to optimize the maintenance activities.

新能源汽车机械系统故障诊断技术研究

新能源汽车的机械系统结构复杂,包含电机、电池、传动装置等多个核心部件,任何一环出现故障都可能引发连锁反应。常见的故障包括电机过热、电池失效、传动系统异常等。为了有效应对这些故障,需要不断提高故障诊断技术的准确性和效率,以便快速定位问题并采取相应的修复措施。通过持续的技术创新和研发,不断提升新能源汽车机械系统的性能,确保车辆在各种场景下都能稳定运行,从而为消费者提供更加安全、可靠的出行体验。

基于人工智能的新能源汽车故障诊断技术

通过故障诊断,可以及时发现新能源汽车的电池、电机、电控系统等关键部件的问题。传统的故障诊断方法主要依赖人工经验,已经无法满足现代新能源汽车日益复杂的故障检测需求。该文探讨了基于人工智能(AI)的故障诊断技术在新能源汽车中的应用,通过深度学习、机器学习和专家系统等AI技术,分析神经网络、支持向量机、决策树等技术在新能源汽车电池管理系统、动力系统、充电系统等关键部件故障诊断中的应用,并提出了未来的研究方向和技术发展趋势。研究结果旨在提高车辆的安全性和可靠性,降低维护成本,逐步提升新能源汽车用户满意度。

“岗课赛证”融通下新能源汽修课程教学设计研究

“岗课赛证”综合育人模式的提出为高职新能源汽车检测与维修技术专业课程改革指明了新的方向。为切实有效提升专业人才培养质量,以专业核心课程纯电动汽车故障诊断为载体,通过调研分析总结岗位需求、技能竞赛、职业技能证书与专业课程的对应关系,进行任务驱动式课程设计及实践评价。探索了一条提升“岗课赛证”深度融通的教学设计方法与路径。为职业教育课程改革提供参考和借鉴,从而培养适应现代新能源汽车行业需求的高素质人才。

Fault detection and diagnosis of electric bus air conditioning systems incorporating domain knowledge and probabilistic artificial intelligence

The air conditioning systems in electric city buses usually operate in rapidly changing ambient conditions and are more likely to suffer from mechanical faults. Although many fault detection and diagnosis (FDD) methods have been developed for building air conditioning systems, they are difficult to be applied to bus air conditioners since its operation is highly dynamic and fault-free data are usually unavailable. Therefore, this paper proposes an FDD method for electric bus air conditioners to tackle the above issues. First, the method identifies faults in an unsupervised manner by comparing selected features among a group of peer systems. Then, considering the features are influenced by the operating conditions, Gaussian process regression (GPR) models are established to find the relationships between each feature and its influential parameters. The probabilistic nature of the GPR is used to differentiate predictions with large uncertainty, which are then excluded from FDD. In this way, robustness of the method is evidently improved. Finally, fault indexes are defined to detect and diagnose mechanical faults. We applied the method to a group of air conditioners in a city bus fleet. Results showed that it can effectively identify refrigerant undercharge and indoor and outdoor fan problems with low false positive/genitive rates. Also, the method is highly robust and not sensitive to the faulty systems in the bus fleet.

基于系统在环的新能源三电系统集成测试方法研究

新能源汽车的“三电系统”(电池、电机、电控)是车辆动力系统的核心,承担着能量储存与管理、驱动控制及整车控制等关键功能。鉴于“三电系统”的高度复杂性和故障敏感性,对其进行系统级验证和故障注入安全测试至关重要。然而,当前行业内缺乏系统化的正向开发三电系统集成台架,单纯依赖部件级测试难以全面覆盖实际运行环境中的多样化场景和工况,而系统级测试主要依赖供应商的零部件测试和整车测试,这种做法可能会严重影响新能源汽车车型的开发进度和品质。为解决这些问题,提出一种新的测试系统:系统层级在环测试系统(System-Level Hardware-in-the-Loop,S-HIL)。S-HIL测试系统通过将真实的三电系统高压部件与仿真的域外被控对象连接,利用实时仿真硬件来模拟,实现一种高效且低成本的三电系统高压部件全面测试方法。

A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions

Fault detection and diagnosis(FDD)approaches comprise three main pillars:model-based,knowledge-based,and data-driven strategies.Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system’s background;yet,significant amounts of data is required,which often poses challenges to researchers.Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods,it has been used in data-driven FDD analysis.However,the majority of FDD approaches are implemented at the system level of buildings.However,most buildings have numerous systems with distinct features.Furthermore,using individualised system-level analysis makes it difficult to see system-to-system relationships.Currently,there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios,so as to identify a wider range of energy consumption related faults in buildings.Furthermore,since data-driven approaches significantly depend on the quantities of training data,it becomes challenging to diagnose faults that have limited features.As a result,this study diagnoses numerous building systems faults,including single and simultaneous faults with limited features.This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas,employing data-driven FDD methodologies.Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes.Furthermore,feature extraction methodologies were compared to quantify their potential for improving the diagnosis.In addition to the classification evaluation metrics,one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences.RF classifier obtained highest classification accuracy during validation and testing with about 90%,indicating a promising performance in whole-building faults analysis.The adoption of feature extraction techniques did not improve classification performance,thereby emphasising that some classifiers may perform better with high-dimensional datasets.

复杂环境新能源汽车永磁电机传感器故障诊断

新能源汽车电机驱动过程中由于电机系统本身噪声、传感器运行特性和各个部件接触不良等因素影响下,永磁电机传感器易处于异常工作状态和故障状态。电动汽车电机运行环境的复杂性导致故障诊断难度也相对较大。于是提出新能源汽车永磁电机传感器故障诊断方法。通过分析永磁电机机械运动方式构建新能源汽车永磁电机数学模型,将上述模型与滑模观测器结合,捕捉任意时刻永磁电机传感器故障状态。基于此,提取传感器故障状态下的特征量,并输入支持向量机中,根据支持向量机输出的分类结果实现新能源汽车永磁电机传感器故障诊断。实验结果表明,针对新能源汽车恒速形式和新能源汽车变速形式,研究方法的诊断结构均与实际情况相符。

新能源汽车动力电池压差故障及维修技术探析

新能源汽车动力电池压差故障是一种常见的故障问题,该故障会严重影响电池性能,不利于新能源汽车正常行驶。为解决这一问题,通过对新能源汽车动力电池压差故障与维修技术进行了分析,首先,简单介绍了新能源汽车的特点,其次,分析了新能源汽车动力电池压差故障问题,包括相应的故障类型、故障原因等,最后,对新能源汽车动力电池压差故障诊断与维修技术进行了分析,系统汇总了新能源汽车动力电池压差故障诊断技术,讨论了相关的维修技术,希望能够为相关研究提供一定的参考。

新能源汽车高压不上电故障诊断与分析

随着新能源汽车的快速发展,其高压电系统的故障诊断和维修变得尤为关键。新能源汽车高压不上电故障诊断是新能源汽车维修中的一个重要环节。高压电系统是新能源汽车的核心部件,包括高压电池、电机控制器、电机等。如果高压电系统出现故障,不仅会影响新能源汽车的性能和安全性,还会导致车辆无法正常行驶。因此,及时准确地诊断和解决高压不上电故障对于保障新能源汽车的正常运行至关重要。

新能源汽车与传统汽车维修有效衔接环境下的特征及需求分析

随着全球气候变化和环境问题日益严重,新能源汽车(NEV)的普及已成为解决能源危机和减少环境污染的重要途径。新能源汽车在结构、动力、电子控制等方面,与传统汽车存在一定差异,技术的特殊性给汽车维修行业带来了新的挑战和机遇。本文从新能源汽车与传统汽车的特征差异入手,探讨了在新能源汽车与传统汽车维修有效衔接的环境下,新能源汽车维修行业的特征,及对维修设备、人才等的需求变化。

智能化技术在新能源汽车故障诊断中的应用

阐述新能源汽车故障诊断的特点,探讨智能化电子诊断技术的应用,包括故障预测、故障定位、故障诊断和故障修复。分析新能源汽车故障诊断中的优势和不足,展望发展趋势。

传感器信息融合下新能源汽车动力电池信号故障检测方法

新能源汽车作为节能环保的新产品具有较好社会前景,内在的动力电池是新能源汽车的主动力源,但电池发动机是一个较为复杂的系统,在处于恶劣环境时,可能出现各种故障问题。新能源汽车的动力电池若发生故障,不但会使汽车的系统性能下降,还会造成灾难性的后果,为此,研究传感器信息融合下新能源汽车动力电池信号故障检测方法。通过一致性定律整理电池系统传感器数据,在近似概率和频率中估算新能源汽车动力电池信号;选择熵权重法理论对数据信号进行区分,以时刻内单体电压作为评价指标,在预处理后构建判断信号故障矩阵;通过故障判断矩阵确定异常信号,在传感器信息融合算法下修订权值,以最大误差范围检测信号输出,检测新能源汽车动力电池信号故障,完成检测方法设计。实验以四组不同类型的新能源汽车作为测试对象,对其动力电池的运动工况进行信号模拟,在不同的接口处获取故障电压信号并完成检测测试,设计的电池信号故障检测方法能够实现精准的故障信号跟踪,完成较为精准的故障信号检测,具有一定的应用价值。

面向新能源汽车的维修站改建评价方法

在发展新能源汽车国家战略的驱动下,从改建现有燃油汽车维修站的角度出发,在兼顾燃油汽车维修需求的基础上,通过构建涵盖社会、环境、企业、政策、交通五大因素20个指标在内的改建评价指标体系,提出面向新能源汽车的TOPSIS-秩和比法维修站改建综合评价方法,对进一步完善维修站评价体系与方法起到积极的作用。同时,为我国汽车维修站转型改建发展提供了基础量化参考。最后,以北京市现有燃油汽车维修站改建为例,对提出方法的可行性进行了应用验证。评价结果表明,最适合改建的燃油汽车维修站主要集中在北京市中心城区,该区交通便利,人口密集,经济发达,有利于新能源汽车维修站的发展。分析建议政府可对相对薄弱的地区采取改建补贴政策、技术人才和维修资源引入等措施。北京市维修站在整体性提升的同时更需关注交通因素相关指标的均等化发展。

新能源汽车驱动电机维护保养与故障维修

新能源汽车一直备受关注,与传统汽车相比,明显的优势为其带来了更多的发展机遇。目前,随着新能源汽车的数量持续增长,相对于新能源汽车后期的保养以及故障维修工作也变得更加重要。驱动电机作为新能源汽车的核心部件之一,在新能源汽车销量不断攀升的情况下,其维护、保养和维修需求也在增加。基于此,本文旨在对新能源汽车驱动电机的维护保养与故障维修进行深入探讨与研究,并提供给相关工作人员参考。

新能源汽车速度传感器故障检测与控制技术分析

随着现代化社会的快速发展,新能源汽车兴起,传感器作为其关键组成部分之一,在推动汽车电动化和智能化发展方面扮演着至关重要的角色。本文基于新能源汽车驱动系统对速度传感器的检测与控制关键进行简单分析,根据分析结果深入探究该装置设备的故障检测与控制技术,最后开展模拟仿真实例分析,以期控制跟踪速度,提高新能源汽车行驶安全性,为相关工作或人员提供有效参考。

新能源汽车的故障及其维修技术研究

本文介绍了新能源汽车的背景和发展现状,分析新能源汽车可能出现的故障后,将其分为变速器故障、驱动电机故障以及空调系统故障等。在此基础之上,分析比较了新能源汽车的检修和维修技术,包括故障诊断技术、维修设备和工具、维修流程和服务能力等,以期为相关工作的开展提供参考。

新能源汽车发动机的维修技术分析

随着新能源汽车市场的迅速扩张,发动机维修技术成为保障车辆性能与安全的关键。文章首先简要分析了新能源汽车发动机常见故障及其原因,以此为基础,详细探讨了针对不同故障的维修方法与策略,包括发动机清洗与检修、传感器检查、轮胎气压及配重检查调节等。展望新能源汽车发动机维修技术的发展趋势,旨在为相关从业人员提供技术指导与参考。