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.

Analysis of Detection and Monitoring Technology in the Construction and Maintenance of Large Bridges

Currently,there is significant attention placed on the construction,management,and maintenance of large service bridges.Within the realm of bridge maintenance management,the utilization of detection and monitoring technology is indispensable.By employing these technologies,we can effectively identify any structural defects within the bridge,promptly uncover unknown risks,proactively establish maintenance strategies,and prevent the rapid deterioration of bridge conditions.This article aims to explore the advantages of applying bridge monitoring and testing technology and to discuss various methods for implementing detection and monitoring technology throughout the construction,management,and maintenance phases of large bridges.Ultimately,this will contribute to ensuring the safe operation of large bridges.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Research of BIM Technology in Urban Rail Operation and Maintenance and Application

With the rapid development of urban rail transit,the traditional operation and maintenance methods mainly rely on manpower,and the pressure on equipment operation and maintenance is increasing,and the low work efficiency,high intensity and high operating costs have always been the main problems at this stage.Through BIM visualization technology can effectively solve those problems,the operation and maintenance mode of urban rail signal equipment at this stage is discussed,the integration and visualization of BIM technology are used to make the judgments and early warnings on the maintenance of urban rail signal equipment,and play a positive and important role in operation and maintenance work,this research result can provide ideas and references for urban rail transit operation and maintenance in the new era.

Research and design of an expert diagnosis system for rail vehicle driven by data mechanism models

Purpose-The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.Design/methodology/approach-The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle.Based on data mechanism models,it predicts the lifespan of key components,evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.Findings-The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system,which helps operators to monitor the operation of vehicle online,predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.Originality/value-This system improves the efficiency of rail vehicle operation,scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

Automation in road distress detection,diagnosis and treatment

Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.

Diagnosis of Mycobacterium tuberculosis using molecular biology technology

Objective:To present an integrated molecular biology dedicated system for tuberculosis diagnosis.Methods:One hundred and five sputum specimens from patients strongly suspected by clinical parameters of tuberculosis were studied by Ziehl-Neelsen staining,by cultivation on solid medium and by a balanced hemincsted fluorometric PCR system(Orange C3TB) that could preserve worker safety and produce a rather pure material free of potential inhibitors. DNA amplification was performed in a low cost tuberculosis termocycler-fluorotneter.Produced double stranded DNA was flurometrically detected.The whole reaction was conducted in one single tube which would not be opened after adding the processed sample in order to minimize the risk of cross contamination with amplicons.Results:The assay was able to delect 30 bacillus per sample mL with 99.8%interassay variation coefficient.PCR was positive in 23(21.9%) tested samples(21 of them were smear negative).In our study it showed a preliminary sensitivity of 94.5%for sputum and an overall specificity of 98.7%.Conclusions:Total run time of the test is 4 h with 2.5 real working time.All PCR positive samples are also positive by microbiological culture and clinical criteria.Results show that it could be a very useful tool to increase detection efficiency of tuberculosis disease in low bacilus load samples.Furthermore,its low cost and friendly using make it feasible to run in poor regions.

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.

Discussion on the Application of Highway Maintenance Platform Based on GIS + BIM Technology—Taking Gansu Province as an Example

Gansu Province is a large province in Western China, and its geographical location and economic status are very important. With the state’s attention to the western development, the economy of the western region represented by Gansu Province has developed rapidly, and the construction of roads and other infrastructure has also been developed. Taking Gansu Province as an example, this paper studies and discusses the application of highway maintenance platform based on GIS + BIM Technology. Firstly, this paper expounds the advantages of GIS + BIM Technology in highway construction and maintenance, and analyzes the development status of GIS + BIM Technology;Secondly, it expounds how to solve the problem of highway maintenance from the aspects of GIS + BIM system and its advantages and difficulties in highway maintenance, and briefly explains the difficulties existing in highway maintenance in Gansu Province. Then, starting from the path of GIS + BIM Technology to solve highway maintenance in Gansu Province, this paper analyzes the expected effect of GIS + BIM Technology in highway maintenance in Gansu Province.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.

An Improved Biometric Fuzzy Signature with Timestamp of Blockchain Technology for Electrical Equipment Maintenance

The power infrastructure of the power system is massive in size and dispersed throughout the system.Therefore,how to protect the information security in the operation and maintenance of power equipment is a difficult problem.This paper proposes an improved time-stamped blockchain technology biometric fuzzy feature for electrical equipment maintenance.Compared with previous blockchain transactions,the time-stamped fuzzy biometric signature proposed in this paper overcomes the difficulty that the key is easy to be stolen by hackers and can protect the security of information during operation and maintenance.Finally,the effectiveness of the proposed method is verified by experiments.

Application of Technology in the Fault Diagnosis of Large Centrifugal Pump Units

In order to promote the stability of centrifugal pump units and maximize the role of centrifugal pumps, this paper analyzes the composition and basic working principle of centrifugal pumps, presents the main concerns of centrifugal pump maintenance, and finally investigates the common faults and maintenance methods of centrifugal pumps for reference.

Disease Characteristics and Maintenance Technology of Highway Subgrade and Pavement

In recent years,the subgrade and pavement diseases account for the vast majority of the expressway maintenance diseases,which puzzles many expressway maintenance departments and units.To sustain the continuous development of the expressway,the service level of the expressway is need to be improved,by ensuring the safety of the people,and by providing more convenient services for the people during travel.The technical measures for scientific maintenance of expressway subgrade and pavement diseases were discussed in this paper.Further,this paper discusses the main subgrade and pavement diseases types,and proposed the targeted maintenance methods,which can be as a reference for the expressway maintenance departments.

Intelligent System Design for Stator Windings Faults Diagnosis:Suitable for Maintenance Work

The short circuit is a severe fault that occurs in the stator windings. Therefore, it is very important to diagnose this type of failure in its beginning before it causes unscheduled stop and the machine loss. In this context, the Support Vector Machine (SVM) is a tool of considerable importance for standard classification. From some training data, it can diagnose whether or not there is a short circuit beginning, and which is important for predictive maintenance. This work proposes a technique for early detection of a short circuit between the turns aiming at its implementation in a real plant. The paper shows simulation and experimental results, and validates the proposed technique.

Failure Prediction and Intelligent Maintenance of a Transportation Company’s Urban Fleet

The present work deals with intelligent vehicle fleet maintenance and prediction. We propose an approach based primarily on the history of failures data and on the geographical data system. The objective here is to predict the date of failures for a fleet of vehicles in order to allow the maintenance department to efficiently deploy the proper resources;we further provide specific details regarding the origins of failures, and finally, give recommendations. This study used the Société de transport de Montréal (STM)’s historical bus failure data as well as weather data from Environment Canada. We thank Facebook’s Prophet, Simple Feed-forward, and Beats algorithms (Uber), we proposed a set of computer codes that allow us to identify the 20% of buses that are responsible for the 80% of failures by mean of the failure history. Then, we deepened our study on the unreliable equipments identified during the diffusion of our computer code This allowed us to propose probable predictions of the dates of future failures. To ensure the validity of the proposed algorithm, we carried out simulations with more than 250,000 data. The results obtained are similar to the predicted theoretical values.

Misdiagnosis and Delayed Diagnosis for Ectopic and Heterotopic Pregnancies after In Vitro Fertilization and Embryo Transfer

This study examined the misdiagnosis and delayed diagnosis factors for ectopic pregnancy（EP） and heterotopic pregnancy（HP） after in vitro fertilization and embryo transfer（IVF-ET） in an attempt to reduce the diagnostic error. Clinical data of patients who underwent IVF-ET treatment and had clinical pregnancy from 12463 cycles were retrospectively analyzed. Their findings of serum β-hCG test and transvaginal ultrasonography were also obtained during follow-up. These patients were divided into two groups according to the diagnosis accuracy of EP/HP： early diagnosis and misdiagnosis/delayed diagnosis. The results showed that the incidence of EP and HP was 3.8%（125/3286） and 0.8%（27/3286） respectively for IVF/ICSI-ET cycle, and 3.8%（55/1431） and 0.7%（10/1431） respectively for frozen-thawed embryo transfer（FET） cycle. Ruptured EP occurred in 28 patients due to initial misdiagnosis or delayed diagnosis. Related factors fell in 3 categories：（1） clinician factors： misunderstanding of patients＇ medical history, insufficient training in ultrasonography and unawareness of EP and HP;（2） patient factors： noncompliance with medical orders and lack of communication with clinicians;（3） complicated conditions of EP： atypical symptoms, delayed elevation of serum β-hCG level, early rupture of cornual EP, asymptomatic in early gestation and pregnancy of unknown location. All the factors were interwoven, contributing to the occurrence of EP and HP. It was concluded that complicated conditions are more likely to affect the diagnosis accuracy of EP/HP after IVF-ET. Transvaginal ultrasonography should be performed at 5 weeks of gestation. Intensive follow-up including repeated ultrasonography and serial serum β-hCG tests should be performed in patients with a suspicious diagnosis at admission.

Fault diagnosis and analysis of main sea water pump based on vibration monitoring in offshore oil field

The main sea water pump is the key equipment for the floating production storage and offloading （FPSO）. Affected by some factors such as hull deformation, sea water corrosion, rigid base and pipeline stress, the vibration value of main sea water pump in the horizontal direction is abnormally high and malfunctions usually happen. Therefore, it is essential to make fault diagnosis of main sea water pump, By conventional off-line monitoring and vibration amplitude spectrum analysis, the fault cycle is found and the alarm value and stop value of equipment are set, which is helpful to equipment maintenance and accident prevention.

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.

Fault Diagnosis Method Based on Fractal Theory and Its Application in Wind Power Systems

The non-linear dynamic theory brought a new method for recognizing and predicting complex non-linear dynamic behaviors. The non-linear behavior of vibration signals can be described by using fractal dimension quantitatively. In this paper, a fractal dimension calculation method for discrete signals in the fractal theory was applied to extract the fractal dimension feature vectors and classified various fault types. Based on the wavelet packet transform, the energy feature vectors were extracted after the vibration signal was decomposed and reconstructed. Then, a wavelet neural network was used to recognize the mechanical faults. Finally, the fault diagnosis for a wind power system was taken as an example to show the method's feasibility.