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.

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.

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.

Fault Diagnosis of Vehicle Transmission System Based on Rough Set Theory

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

Role of Plain Abdominal X-Ray in the Differential Diagnosis of Common Acute Abdominal Conditions in Children at Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State, Nigeria

Background: The plain abdominal x-ray is one of the commonly requested investigations in the children emergency room, paediatric surgical ward and neonatal wards. The short interval required to carry out this investigative procedure and obtain results makes it the first imaging modality used to unravel the different causes of acute abdominal conditions in children. The safety of abdominal x-ray in children makes it attractive for use in paediatric surgical practice as part of routine work-up for undifferentiated acute abdominal conditions and also to diagnose specific causes of acute abdomen in children. Setting: Olabisi Onabanjo University Teaching Hospital, Sagamu, Ogun State. Objectives: Evaluation of the role of plain abdominal x-ray in diagnosing common acute abdominal conditions in children. Materials and method: Patients admitted to the children emergency room, paediatric surgical wards, children’s ward and neonatal ward who had plain abdominal x-ray as part of their diagnostic work-up were included in the study. They were studied prospectively between March 2011 and April 2021. Results: Three Hundred and Ninety-nine patients who had plain abdominal x-rays as part of their diagnostic work-up were studied. Males were 240 while females were 159, a male to female ratio of 1.5:1. The patients were aged between 1 day to 16 years. Differential diagnoses made with plain abdominal x-ray were intestinal obstruction in 298, perforated viscus 69 patients, intra-abdominal masses 13 patients and location of intra-abdominal foreign body 14. Intestinal obstruction cases in which plain abdominal x-ray played a role in their diagnosis and management included the following: intussusception 66, neonatal sepsis 60, malrotation 48, intestinal atresia 42, anorectal malformation 32, hirschsprung’s disease in 30 cases, pyloric stenosis 24, obstructed hernia 22, post-operative adhesions 16 and intestinal helminthiasis 12. Perforated viscus accounted for 69 indications. Out of these indications, perforated gut in intussusception 19, perforated typhoid ileitis was responsible in 13 cases, gut perforation in blunt abdominal trauma 8, perforation in strangulated hernia 11 cases, perforated gut in malrotation 7, ceacal perforation in hirschsprugs disease 6 and colonic perforation in necrotizing enterocolitis 5 cases. Conclusion: Plain abdominal x-ray remains a role to play in the differential diagnosis and management of common paediatric acute abdominal conditions.

Multi-Fault Diagnosis for Autonomous Underwater Vehicle Based on Fuzzy Weighted Support Vector Domain Description

This paper addresses the multi-fault diagnosis problem of thrusters and sensors for autonomous underwater vehicles (AUVs). Traditional support vector domain description (SVDD) has low classification accuracy in the process of AUV multi-fault pattern classification because of the effect of sample sparse density and the uneven distribution of samples, and so on. Thus, a fuzzy weighted support vector domain description (FWSVDD) method based on positive and negative class samples is proposed. In this method, the negative class sample is introduced during classifier training, and the local density and the class weight are introduced for each sample. To improve the multi-fault pattern classifier training speed and fault diagnosis accuracy of FWSVDD, a multi-fault mode classification method based on a hierarchical strategy is proposed. This method adds fault contain detection surface for each thruster and sensor to isolate fault components during fault diagnosis. By considering the problem of pattern classification for a fuzzy sample, which may be located in the overlapping area of hyper-spheres or may not belong to any hyper-sphere in the process of multi-fault classification based on FWSVDD, a relative distance judgment method is given. The effectiveness of the proposed multi-fault diagnosis approach is demonstrated through water tank experiments with an experimental AUV prototype.

Deep Learning Based Data Fusion for Sensor Fault Diagnosis and Tolerance in Autonomous Vehicles

Environmental perception is one of the key technologies to realize autonomous vehicles.Autonomous vehicles are often equipped with multiple sensors to form a multi-source environmental perception system.Those sensors are very sensitive to light or background conditions,which will introduce a variety of global and local fault signals that bring great safety risks to autonomous driving system during long-term running.In this paper,a real-time data fusion network with fault diagnosis and fault tolerance mechanism is designed.By introducing prior features to realize the lightweight network,the features of the input data can be extracted in real time.A new sensor reliability evaluation method is proposed by calculating the global and local confidence of sensors.Through the temporal and spatial correlation between sensor data,the sensor redundancy is utilized to diagnose the local and global confidence level of sensor data in real time,eliminate the fault data,and ensure the accuracy and reliability of data fusion.Experiments show that the network achieves state-of-the-art results in speed and accuracy,and can accurately detect the location of the target when some sensors are out of focus or out of order.The fusion framework proposed in this paper is proved to be effective for intelligent vehicles in terms of real-time performance and reliability.

Actuator fault diagnosis of autonomous underwater vehicle based on improved Elman neural network

Autonomous underwater vehicles(AUV) work in a complex marine environment. Its system reliability and autonomous fault diagnosis are particularly important and can provide the basis for underwater vehicles to take corresponding security policy in a failure. Aiming at the characteristics of the underwater vehicle which has uncertain system and modeling difficulty, an improved Elman neural network is introduced which is applied to the underwater vehicle motion modeling. Through designing self-feedback connection with fixed gain in the unit connection as well as increasing the feedback of the output layer node, improved Elman network has faster convergence speed and generalization ability. This method for high-order nonlinear system has stronger identification ability. Firstly, the residual is calculated by comparing the output of the underwater vehicle model(estimation in the motion state) with the actual measured values. Secondly, characteristics of the residual are analyzed on the basis of fault judging criteria. Finally, actuator fault diagnosis of the autonomous underwater vehicle is carried out. The results of the simulation experiment show that the method is effective.

Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network

This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.

Computer-Aided Diagnosis for Tuberculosis Classification with Water Strider Optimization Algorithm

Computer-aided diagnosis(CAD)models exploit artificial intelligence(AI)for chest X-ray(CXR)examination to identify the presence of tuberculosis(TB)and can improve the feasibility and performance of CXR for TB screening and triage.At the same time,CXR interpretation is a time-consuming and subjective process.Furthermore,high resemblance among the radiological patterns of TB and other lung diseases can result in misdiagnosis.Therefore,computer-aided diagnosis(CAD)models using machine learning(ML)and deep learning(DL)can be designed for screening TB accurately.With this motivation,this article develops a Water Strider Optimization with Deep Transfer Learning Enabled Tuberculosis Classification(WSODTL-TBC)model on Chest X-rays(CXR).The presented WSODTL-TBC model aims to detect and classify TB on CXR images.Primarily,the WSODTL-TBC model undergoes image filtering techniques to discard the noise content and U-Net-based image segmentation.Besides,a pre-trained residual network with a two-dimensional convolutional neural network(2D-CNN)model is applied to extract feature vectors.In addition,the WSO algorithm with long short-term memory(LSTM)model was employed for identifying and classifying TB,where the WSO algorithm is applied as a hyperparameter optimizer of the LSTM methodology,showing the novelty of the work.The performance validation of the presented WSODTL-TBC model is carried out on the benchmark dataset,and the outcomes were investigated in many aspects.The experimental development pointed out the betterment of the WSODTL-TBC model over existing algorithms.

Comparing upper gastrointestinal X-ray and endoscopy for gastric cancer diagnosis in Korea

AIM:To compare the cost and accuracy of upper gastrointestinal(GI)X-ray and upper endoscopy for diagnosis of gastric cancer using data from the 2002-2004 Korean National Cancer Screening Program(NCSP). METHODS:The study population included 1 503 646 participants in the 2002-2004 stomach cancer screening program who underwent upper GI X-ray or endoscopy.The accuracy of screening was defined as the probability of detecting gastric cancer.We calculated the probability by merging data from the NCSP and the Korea Central Cancer Registry.We estimated the direct costs of the medical examination and the tests for up- per GI X-ray,upper endoscopy,and biopsy. RESULTS:The probability of detecting gastric cancervia upper endoscopy was 2.9-fold higher than via upper GI X-ray.The unit costs of screening using upper GI X-ray and upper endoscopy were$32.67 and$34.89, respectively.In 2008,the estimated cost of identifying one case of gastric cancer was$53094.64 using upper GI X-ray and$16 900.43 using upper endoscopy.The cost to detect one case of gastric cancer was the same for upper GI X-ray and upper endoscopy at a cost ratio of 1:3.7. CONCLUSION:Upper endoscopy is slightly more costly to perform,but the cost to detect one case of gastric cancer is lower.

Screening and early diagnosis of osteoporosis through X-ray and ultrasound based techniques

Effective prevention and management of osteoporosis would require suitable methods for population screenings and early diagnosis. Current clinicallyavailable diagnostic methods are mainly based on the use of either X-rays or ultrasound(US). All X-ray based methods provide a measure of bone mineral density(BMD), but it has been demonstrated that other structural aspects of the bone are important in determining fracture risk, such as mechanical features and elastic properties, which cannot be assessed using densitometric techniques. Among the most commonly used techniques, dual X-ray absorptiometry(DXA) is considered the current 'gold standard' for osteoporosis diagnosis and fracture risk prediction. Unfortunately, as other X-ray based techniques, DXA has specific limitations(e.g., use of ionizing radiation, large size of the equipment, high costs, limited availability) that hinder its application for population screenings and primary care diagnosis. This has resulted in an increasing interest in developing reliable pre-screening tools for osteoporosis such as quantitative ultrasound(QUS) scanners, which do not involve ionizing radiation exposure and represent a cheaper solution exploiting portable and widely available devices. Furthermore, the usefulness of QUS techniques in fracture risk prediction has been proven and, with the last developments, they are also becoming a more and more reliable approach for assessing bone quality. However, the US assessment of osteoporosis is currently used only as a pre-screening tool, requiring a subsequent diagnosis confirmation by means of a DXA evaluation. Here we illustrate the state of art in the early diagnosis of this 'silent disease' and show up recent advances for its prevention and improved management through early diagnosis.

Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

In today’s world,smart electric vehicles are deeply integrated with smart energy,smart transportation and smart cities.In electric vehicles(EVs),owing to the harsh working conditions,mechanical parts are prone to fatigue damages,which endanger the driving safety of EVs.The practice has proved that the identification of periodic impact characteristics(PICs)can effectively indicate mechanical faults.This paper proposes a novel model-based approach for intelligent fault diagnosis ofmechanical transmission train in EVs.The essential idea of this approach lies in the fusion of statistical information and model information froma dynamic process.In the algorithm,a novel fractal wavelet decomposition(FWD)is used to investigate the time-frequency representation of the input signal.Based on the sparsity of the PIC model in the Hilbert envelope spectrum,amethod for evaluating PIC energy ratio(PICER)is defined based on an over-complete Fourier dictionary.A compound indicator considering kurtosis and PICER of dynamic signal is designed.Using this index,evaluations of the impulsiveness of the cycle-stationary process can be enabled,thus avoiding serious interference from the sporadic impact during measurements.The robustness of the proposed approach to noise is demonstrated via numerical simulations,and an engineering application is employed to validate its effectiveness.

A Comprehensive Investigation of Machine Learning Feature Extraction and ClassificationMethods for Automated Diagnosis of COVID-19 Based on X-ray Images

The quick spread of the CoronavirusDisease(COVID-19)infection around the world considered a real danger for global health.The biological structure and symptoms of COVID-19 are similar to other viral chest maladies,which makes it challenging and a big issue to improve approaches for efficient identification of COVID-19 disease.In this study,an automatic prediction of COVID-19 identification is proposed to automatically discriminate between healthy and COVID-19 infected subjects in X-ray images using two successful moderns are traditional machine learning methods(e.g.,artificial neural network(ANN),support vector machine(SVM),linear kernel and radial basis function(RBF),k-nearest neighbor(k-NN),Decision Tree(DT),andCN2 rule inducer techniques)and deep learningmodels(e.g.,MobileNets V2,ResNet50,GoogleNet,DarkNet andXception).A largeX-ray dataset has been created and developed,namely the COVID-19 vs.Normal(400 healthy cases,and 400 COVID cases).To the best of our knowledge,it is currently the largest publicly accessible COVID-19 dataset with the largest number of X-ray images of confirmed COVID-19 infection cases.Based on the results obtained from the experiments,it can be concluded that all the models performed well,deep learning models had achieved the optimum accuracy of 98.8%in ResNet50 model.In comparison,in traditional machine learning techniques, the SVM demonstrated the best result for an accuracy of 95% and RBFaccuracy 94% for the prediction of coronavirus disease 2019.

Exploratory Application of X-ray Photography in the Diagnosis of Poultry Fatty Liver

[Objectives]This study was conducted to explore the application value of X ray in the diagnosis of poultry fatty liver.[Methods]Serum biochemical tests were performed on laying hens with suspected fatty liver.The X-ray liver images of the left lateral position were observed.And the liver traits were observed by anatomy.[Results]The increase in liver space occupying lesion observed by X-ray photography was consistent with the increase in liver volume observed by anatomy.The biochemical test showed slight liver function abnormalities.[Conclusions]X-ray examination can be used for the auxiliary diagnosis of poultry fatty liver.

X-ray DIAGNOSIS OF EXTRASKELETAL (SOFT TISSUE) CHONDROSARCOMA (A REPORT OF 8 CASES)

Eight cases of surgically and pathologically verified extraskeletal (soft tissue) chondrosarcoma were analyzed with regard to clinical and X-ray features. The cardinal clinical aspects of this series are: presence of a local soft tissue mass; gradual enlargement of the mass accompanied by increasing pain. The X-ray signs were summarized as follows: formation of a soft tissue mass; various forms of calcifications concentrated in the central area of the tumor; in some instances, presence of a saucer-like defect on the cortical surface of neighbouring bone and periosteal proliferation with mound-like new bone on both sides as well as bending deformity of the affected bone. The incidence and sites of predilection, the main X-ray findings, radiological diagnosis and differential diagnosis of the tumor were discussed. The Roentgen features of synovial chondrosarcoma of the knee joint were especially analyzed.

X-ray DIAGNOSIS OF JUXTACORTICAL CHONDROSARCOMA (A REPORT OF 16 CASES)

The clinical and radiologic features of 16 cases of surgically and pathologically proved juxtacortical chondrosarcoma were analyzed. The main radiograp-hic findings include: (1) A saucerlike or scalloped defect confined to the outer surface of cortex dut to tumor erosion; (2) Localized cortical proliferation and sclerosis; (3) Soft tissue mass and various forms of calcification within the tumor; but without a bony or calcified shell around the tumor mass and (4) sunray and/or laminated periosteal reaction. The origin of this tumor, its histopathology and classification were discussed. A preliminary investigation on the radiated periosteal reaction and its pathological basis was performed.

Development of Management Systems for Electric Vehicle Battery Series

A kind of management system for electric vehicle (EV) battery series was developed. The system can predict residual capacity for EV battery series and mileages. The system can determine if it is necessary for the battery series to be charged. The system can determine which battery is necessary to be updated for the reason of damage or aging. The system can display the total voltage of battery series, extreme voltage and temperature of every battery in the series. The system can display the accumulative discharge for every battery in the series. The system can alarm when both total or extreme voltage is at low level, or temperature of a battery in the series is at high level. The system provided with a microprocessor as key part can collect and record signal of charging and discharging current, total voltage, extreme voltage and temperature for every battery. The mathematical model of residual capacity for EV lead acid batteries was discussed in details. The system operates well in the laboratory and meets the requirement.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems,especially in the case of the medical field.Among lung problems,tuberculosis,caused by a bacterium called Mycobacterium tuberculosis,is a dangerous disease because of its infection and damage.When an infected person coughs or sneezes,tiny droplets can bring pathogens to others through inhaling.Tuberculosis mainly damages the lungs,but it also affects any part of the body.Moreover,during the period of the COVID-19(coronavirus disease 2019)pandemic,the access to tuberculosis diagnosis and treatment has become more difficult,so early and simple detection of tuberculosis has been more and more important.In our study,we focused on tuberculosis diagnosis by using the chestX-ray image,the essential input for the radiologist’s profession,and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images.We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types.We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models.Our experiments were carried out by applying three different architectures,Alexnet,Resnet,and Densenet,on international,Vietnamese,and combined X-ray image datasets.After training,all models were verified on a pure Vietnamese X-rays set.The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC(Area under the Receiver Operating Characteristic Curve),sensitivity,specificity,and accuracy.In the best strategy,most of the scores were more than 0.93,and all AUCs were more than 0.98.