Automatic Fault Diagnosis System Research Articles

A Novel Electric Motor Fault Diagnosis by Using a Convolutional Neural Network, Normalized Thermal Images and Few-Shot Learning

Motors constitute one critical part of industrial production and everyday life. The effective, timely and convenient diagnosis of motor faults is constantly required to ensure continuous and reliable operations. Infrared imaging technology, a non-invasive industrial fault diagnosis method, is usually applied to detect the equipment status in extreme environments. However, conventional Infrared thermal images inevitably show a large amount of noise interference, which affects the analysis results. In addition, each motor may only possess a small amount of fault data in practice, as collecting an infinite amount of motor data to train the diagnostic system is impossible. To overcome these problems, a novel automatic fault diagnosis system is proposed in this study. Data features are enhanced by a normalization module based on color bars first, as the same color in various infrared thermal images represent different temperatures. Then, the few-shot learning method is used to diagnose the faults of unseen electric motors. In the few-shot learning method, the minimum dataset size required to expand system universality is fifteen pieces, effectively solving the universality problem of artificial-to-natural data migration. The method saves a large amount of training data resources and the experimental training data collection. The accuracy of the fault diagnosis system achieved 98.9% on similar motor datasets and 91.8% on the dataset of motors that varied a lot from the training motor, which proves the high reliability and universality of the system.

Fault Diagnosis of Gearbox Using Machine Learning Approach

Gear box is crucial in industrial processes, enabling adjustments of speed and load conditions to meetoperational needs. As gearboxtechnologyadvances, their capabilities increase, but component failure can result in product losses and maintenance costs. Detecting potential failures before handis essential, and vibration measurementis a proven method for monitoring machine condition and predictinggearboxfaults. This study explores the use of machine learning to develop an automated fault diagnosis system for gear boxes using vibration signals. The performance of the developed model is compared with existing methods to determine the most effective algorithm. This research paper explores the application of machine learning techniques for faultdiagnosis of gear boxes using vibration signals. The study involved collecting vibration data fromgearboxes in both good and defective conditions, under various loading conditions. Statistical features were extracted from the collected data and used to develop a fault identification system. The performance of the developed model was evaluated and compared with existingmethods. The study also aimed to determine the mostsuitablealgorithm for the collected data. Overall, the paper provides insights into the effectiveness of using machine learning approaches for gear box fault diagnosis and identifies the best-performing algorithm for this task. Key Word: Machine Learning, Gearbox, Vibration, Data preprocessing, Algorithms.

Improving Efficiency of Angular Velocity Sensors on Aircraft

With the development of science and technology, intelligent systems on aircraft help users know the device's operating status in real time. Using smart devices shortens the time for maintenance, repair, and operation of ground equipment and aircraft equipment. Therefore, building devices capable of self-diagnosis and warning failure are essential in aeronautical engineering. In many published studies, the authors often use the foundation of classic algorithms such as genetics, neural networks, and AI to solve the problem of identification and troubleshoot some simple devices. In Vietnam, there are currently not many published studies on failure diagnosis in aviation engineering, so the author's research has built the foundation for developing studies on fault diagnosis crash in the future. The primary purpose of the research is to create a complete automatic fault diagnosis and repair system for a specific class of inductance (angular speed sensor). The algorithms proposed in the paper are simulated on Matlab Simulink software, which will prove the feasibility of the proposed algorithm. In future studies, the author will apply new algorithms to build more complex fault diagnosis systems for other objects on the flying device.

Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults.

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.

Design and implementation of automatic fault diagnosis system for wind turbine

Operation of wind turbines under fault state will directly affect the power output efficiency of wind farms. This paper proposes a new automatic fault diagnosis method for wind turbines. A fault diagnosis system framework is constructed and data of vibration status of wind turbines collected is processed and used for fault diagnosis. Firstly, wavelet coefficients are obtained using a discrete wavelet transform (DWT) for vibration acceleration signals collected from wind turbines. Then, the wavelet coefficients are sequentially subjected to phase space reconstruction (PSR) and singular value decomposition (SVD) to extract the fault features. Finally, an extreme learning machine (ELM) is used to classify the faults. Experimental results show that the proposed method is more effective and accurate than other fault diagnosis methods for wind turbines, such as support vector machine (SVM) and multiscale convolutional neural network (MSCNN).

Fault diagnosis of adaptive beam using the Mahalanobis–Taguchi system

The application of adaptive structures (smart structures) in ultrahigh precision space structures such as X-ray astronomical satellites is being studied extensively. To ensure the reliability of such ultrahigh precision space structures, it is necessary to introduce an automated fault diagnosis system. In this study, the Taguchi method (Mahalanobis–Taguchi system) is applied to the self-diagnosis problem, wherein a damage in the smart structure is detected using the vibration response data of a smart beam. This article reports on the preliminary results of hardware experiments using a smart beam model in the laboratory.

Fault severity recognition based on self-adaptive particle swarm optimisation using wavelet kernel extreme learning machine

Gear fault levels are an important issue and can show the operating status of gears. In this paper, the extreme learning machine (ELM) learning method is regarded as a basic algorithm for classifying gear fault levels. In order to improve the generalisation of the ELM and obtain a better classification performance, the wavelet kernel concept was introduced to the ELM and thereby a structure called a wavelet kernel extreme learning machine (WK-ELM) was constructed. In terms of the WK-ELM, because the dilation factor of the wavelet kernel and the number of hidden neurons of the ELM are important for the classification performance, particle swarm optimisation (PSO) was used to search for the most appropriate values of the solved problem. What is more, PSO was also optimised by a self-adaptive inertia weight that formed a novel structure, that is, self-adaptive inertia weight particle swarm optimisation (SAPSO). So far, an automatic fault diagnosis system for gear fault levels based on the SAPSO-WK-ELM has been proposed in this paper. Aimed at recognising different gear fault levels at different rotational speeds, ranking mutual information (RMI) and standardised mutual information (SMI) techniques were applied to select sensitive features for gear fault levels. Compared with other relational algorithms previously used, the results of this study, including simulation and experimental data, have demonstrated that the SAPSO-WK-ELM can achieve an outstanding classification performance.

Rolling element bearing fault diagnosis using convolutional neural network and vibration image

Detecting in prior bearing faults is an essential task of machine health monitoring because bearings are the vital components of rotary machines. The performance of traditional intelligent fault diagnosis methods depend on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Recently, deep learning algorithms have been applied widely in machine health monitoring. With the capacity of automatically learning complex features of input data, deep learning architectures have great potential to overcome drawbacks of traditional intelligent fault diagnosis. This paper proposes a method for diagnosing bearing faults based on a deep structure of convolutional neural network. Using vibration signals directly as input data, the proposed method is an automatic fault diagnosis system which does not require any feature extraction techniques and achieves very high accuracy and robustness under noisy environments.

Development of a new automatic fault diagnosis system for fine pattern transmission lines

This paper presents a new automatic fault diagnosis and detection system for fine pattern interconnects. It is verified by performance of the proposed system for COFs (chip-on-films) with width of less than 20 μm and pitch of less than 25 μm as fine pattern transmission lines. The proposed system detects and diagnoses variety of faults such as open, short, near open (mouse bite) and near short (soft short) after fabrication cycle of fine pattern transmission lines. This system consists of RF source, variable gain amplifier (VGA), multi-lead probe card, AC generator, Look-Up Table (LUT), signal processing block, and monitoring block. It also utilizes new techniques to differentiate very small voltage changes due to the faults using VGA and programmable gain amplifier (PGA), and to detect barely detectable far-end near open and near short faults. The proposed system showed very low test overhead and significantly high fault coverage as conventional test systems. It is expected that this system is an effective alternative to decrease test overhead and increase fault coverage for the fine pattern transmission lines.

An Efficient Simplified Physical Faulty Model of a Permanent Magnet Synchronous Generator Dedicated to Stator Fault Diagnosis Part II: Automatic Stator Fault Diagnosis

This paper proposes an automatic and intelligent stator fault diagnosis system for permanent magnet synchronous generators. The system is based on the use of a feedforward multilayer perception artificial neural network (ANN) performed by the back-propagation training algorithm. From a thorough study and analysis of the behavior of the negative-sequence voltage of the machine under different stator faults and different operating conditions, two new robust indicators of faults are selected as the ANN inputs. The two fault indicators are the phase angle of the negative sequence voltage and the frequency of the machine voltages. After successfully training and testing the ANN with specific and meaningful databases, the ANN behavior is validated by using an experimental database acquired from a real machine. The accurate results provided by the ANN prove the reliability of the proposed system to automatically diagnose different stator faults under variable speed, variable fault current, and variable load conditions overcoming different disturbances, such as the noisy signals and the harmonics in the machine.

Sound Based Machine Fault Diagnosis System Using Pattern Recognition Techniques

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

Bearing Fault Diagnosis Using a Novel Classifier Ensemble Based on Lifting Wavelet Packet Transforms and Sample Entropy

In order to improve the fault detection accuracy for rolling bearings, an automated fault diagnosis system is presented based on lifting wavelet packet transform (LWPT), sample entropy (SampEn), and classifier ensemble. Bearing vibration signals are firstly decomposed into different frequency subbands through a three-level LWPT, resulting in a total of 8 frequency-band signals throughout the third layers of the LWPT decomposition tree. The SampEns of all the 8 components are then calculated as feature vectors. Such a feature extraction paradigm is expected to depict complexity, irregularity, and nonstationarity of bearing vibrations. Moreover, a novel classifier ensemble is proposed to alleviate the effect of initial parameters on the performance of member classifiers and to improve classification effectiveness. Experiments were conducted on electric motor bearings considering various set of fault categories and fault severity levels. Experimental results demonstrate the proposed diagnosis system can effectively improve bearing fault recognition accuracy and stability in comparison with diagnosis methods based on a single classifier.

Diagnosis of Nuclear Power Plant Based on Probabilistic Neural Network

This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimize possible nuclear disasters. Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. PNN is fit to diagnose the fault of nuclear power plant and has auto-adaptability. The method not only makes the original neural network smaller in terms of computation and realization, but also improves diagnosis speed and accuracy of nuclear power plant.

A Framework Design of Automatic Fault Diagnosis System

To resolve a lasting suitable cabin environment for the astronauts, this paper proposes an effective framework design for automatic fault diagnosis system. This framework can implement a real-time online diagnosis and decision support for fault, and carry out an early diagnosis for weak fault. Finally, this paper achieves an online automatic fault diagnosis system by using neural networks self-learning characteristics and expert knowledge. In two-men-two-days simulated manned space flight test, the software of diagnosis system framework worked well, which has been assessed and verified comprehensively.

Fault Diagnosis System Based on Fuzzy-inference

Aimed at deficiencies of the traditional fault diagnosis method for pneumatic press, an automatic fault diagnosis system is established and its purpose including improvement of preferment and precision was experimented. First, the fuzzy inference algorithm is analyzed. Then the fuzzy relationship between the fault symptom and the fault cause is compared. Finally, the paper takes the pneumatic press as an example, and established a fast and efficient fault diagnosis system based on fuzzy inference. The practical test results show that the accuracy of fault diagnosis and forecast is improved, and the fuzzy reference algorithm can satisfy the system requirements of security and stabilization as well as higher precision and rapid speed.

Automatic HVAC fault detection and diagnosis system generation based on heat flow models

This article introduces a new graph-based modeling methodology called heat flow modeling (HFM) for the purpose of mapping building information model (BIM) of HVAC systems automatically into fault detection and diagnosis (FDD) systems that can be integrated into HVAC control systems. The goal is an efficient and effective support of the maintenance of HVAC systems to detect and locate faults that may reduce energy efficiency, user comfort, or system lifetime. The nodes of the HFM model have a one-to-one relationship with HVAC system components and related building entities. The nodes are connected by arcs that model the flows in the HVAC systems, e.g., air, water, and information flows. The functionality of the nodes includes state variable estimations and failure rule evaluations. The failure rule outputs can be fed to an associative network based diagnosis engine to locate the faults. Since HFM nodes are instances of generic classes derived from small libraries, HVAC FDD systems can be automatically generated. The simulation result has shown the effectiveness of a proposed FDD approach and two software prototypes demonstrating the reduced engineering effort of fault detection for a small bank HVAC system.

Fault diagnosis of nuclear power plant based on genetic-RBF neural network

This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimise the possible nuclear disasters caused by inaccurate diagnoses done by operators. Combined binary and decimal coding methods are employed in this work based on Radial Basis Function Neural Network (RBFNN) structure. This underlying RBFNN structure is further trained through genetic optimisation algorithm based on known frequent failure conditions from a nuclear power plant's condensation and feed-water system. It is found that the proposed Genetic-RBFNN (GRBFNN) method not only makes the original neural network smaller in terms of computation and realisation but also improves the diagnosis speed and accuracy.

Nuclear power plant fault diagnosis based on genetic-RBF neural network

It is necessary to develop an automatic fault diagnosis system to avoid a possible nuclear disaster caused by an inaccurate fault diagnosis in the nuclear power plant by the operator. Because Radial Basis Function Neural Network (RBFNN) has the characteristics of optimal approximation and global approximation. The mixed coding of binary system and decimal system is introduced to the structure and parameters of RBFNN, which is trained in course of the genetic optimization. Finally, a fault diagnosis system according to the frequent faults in condensation and feed water system of nuclear power plant is set up. As a result, Genetic-RBF Neural Network (GRBFNN) makes the neural network smaller in size and higher in generalization ability. The diagnosis speed and accuracy are also improved.

A LAN fault diagnosis system

The increasing importance of computer networks in today's society demands high levels of network availability and reliability. This is particularly apparent for the enterprises that perform their transactions depending highly on e-commerce. However, a trivial network fault could cause costly damages to the enterprises because of fault propagation. In this paper, we offer a fault diagnosis mechanism for effective and automated network fault isolation. This mechanism uses finite state machine (FSM) to model our classified and refined fault propagation behavior. A concept called fault propagation duration is also introduced into the mechanism and combined with the FSM-based fault propagation knowledge to complete the mechanism's implementation. Based on the diagnosis mechanism, an automated fault diagnosis system called alarm correlation view (or ACView) for isolating network faults is proposed. This diagnosis system not only provides the process of automated alarm collection, alarm correlation, and fault isolation, but also functions to recognize the propagations of all the faults with their corresponding fault severities.

An Automated Fault Diagnosis System Using Hierarchical Reasoning and Alarm Correlation

The increasing importance of computer networks in this information age demands a high level of network availability and reliability. As we become more dependent on networks in our so-called cyber-world, network faults and downtime become very costly. Sometimes, a slight fault may cause critical disruptions or remediless damages to the network while the network manager is lost among a large amount of alarm messages. Therefore, the development of a practical and effective system for network fault diagnosis becomes an imperative and critical task. In this paper, we develop a hierarchical domain-oriented reasoning mechanism suitable for the delegated management architecture. It is based on the causality graph of a refined network fault propagation model as a result of our empirical study. An automated fault diagnosis system called Alarm Correlation View (or ACView) for isolating network faults in a multi-domain environment is proposed according to the hierarchical reasoning mechanism. This diagnosis system not only provides the process of automated alarm collection and correlation, but also serves the function of efficient fault localization and identification. Furthermore, an alarm-to-fault mapping strategy is used to enhance the fault reasoning capability for uncertain network fault propagation.