Intelligent Fault Diagnosis System Research Articles

The Study of Fault Diagnosis the High-Voltage Circuit Breaker Based on Neural Network and Expert System

An intelligent fault diagnosis system based on neural network and expert system integration is studied. The system uses neural networks to acquire fault codes and the corresponding type of fault, which are stored the knowledge base. In fault diagnosis, the expert system uses fault codes that can be found the corresponding fault type from the knowledge base through forward reasoning, and gives a reasonable solution. Meanwhile, this system also overcomes some shortcomings that the neural network doesn’t have functional and expert system has difficult to acquire knowledge. Used neural network and expert system in high-voltage circuit breaker fault diagnosis, the simulation indicates that this method is effective and feasible.

Agent oriented intelligent fault diagnosis system using evidence theory

Multi sensors fusion is a very important process for fault diagnosis system. Information obtained from multi sensors need to be fused because no single sensor can get all the information for fault diagnosis. Moreover, information from different sensors may be uncertainty, inaccuracy, or even conflicting. Evidence theory can be used for information fusion, which is regarded as an extension form of Bayesian reasoning, but it has a better fusion result by simple reasoning process using belief function without knowing the prior probability. All the information collected from multi sensors in the system can be described as the evidence for diagnosis so that the fault diagnosis problem can then be modeled as a problem of evidence fusion and decision. In this paper, the classical Dempster–Shafer evidence theory is discussed, and the disadvantages of the combination rule are also analyzed. The notion of support degree of focal element is suggested in order to evaluate the conflicts between multi sensors. The new combination rule is then built to allocate the conflicted information from multi sensors based on the support degree of focal element. Furthermore, the decision rules for fault diagnosis are also proposed, as well as the architecture of the agent oriented intelligent fault diagnosis system. Finally, a case study is given to illustrate the performance of the proposed model.

The Intelligent Fault Diagnosis of Construction Machinery Based on Multi-Agent

The number of construction machinery faults is too much and its diagnosis are fuzzy and complex. We construct the fault hierarchy model by the means of hierarchy analysis and obtain all kinds of Possibility degree of faults factors through the triangular fuzzy complementary judgment matrix .Take the fault diagnosis of diesel engine fuel system, this paper establish intelligent fault diagnosis system, which has the ability of self-learning and self-correction using multi-agent technology.

The Intelligent Fault Diagnosis of Construction Machinery Based on Multi-Agent

The number of construction machinery faults is too much and its diagnosis are fuzzy and complex. We construct the fault hierarchy model by the means of hierarchy analysis and obtain all kinds of Possibility degree of faults factors through the triangular fuzzy complementary judgment matrix .Take the fault diagnosis of diesel engine fuel system, this paper establish intelligent fault diagnosis system, which has the ability of self-learning and self-correction using multi-agent technology.

Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion

From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

Study on CBR-Based Automobile Engine Intelligent Fault Diagnosis Technique

It is difficult to do the fault diagnosis on the modern car engines which have high technology and complex structures. In this study, a case-based-reasoning (CBR) based automobile engine intelligent fault diagnosis system was proposed against this problem. The system’s structure and its mechanism of fault diagnosis were introduced. The key techniques to implement the system were analyzed, including the case establishment, the case search, the case learning and the maintenance of case library. The proposed system gave a new way to establish an efficient automobile engine fault diagnosis system.

The Research of Intelligent Fault Diagnosis Model on Multi-Information Fusion

From the intelligent fault diagnosis system requirements, this article analyzes the relationship between the fault diagnosis and the multi-information fusion basing on the summing up the multi-sensor information fusion technology, and studies the hierarchical structure of multi-sensor information fusion system and the content of integration, and establishes an intelligent fault diagnosis model with the multi-information fusion, which provides strong support for large-scale equipments, system monitoring and fault diagnosis in production process.

Intelligent fault classification of a tractor starter motor using vibration monitoring and adaptive neuro-fuzzy inference system

This paper presents an intelligent method for fault diagnosis of the starter motor of an agricultural tractor, based on vibration signals and an Adaptive Neuro-Fuzzy Inference System (ANFIS). The starter motor conditions to be considered were healthy, crack in rotor body, unbalancing in driven shaft and wear in bearing. Thirty-three statistical parameters of vibration signals in the time and frequency domains were selected as a feature source for fault diagnosis. A data mining filtering method was performed in order to extract the superior features among the primary thirty-three features for the classification process and to reduce the dimension of features. In this study, six superior features were fed into an adaptive neuro-fuzzy inference system as input vectors. Performance of the system was validated by applying the testing data set to the trained ANFIS model. According to the result, total classification accuracy was 86.67%. This shows that the system has great potential to serve as an intelligent fault diagnosis system in real applications.

Real-time fault diagnostic system for a steam turbine generator set by using a fuzzy cerebellar model articulation controller

Because a serious fault would result in a reduced amount of electricity supply in a power plant, the real-time fault diagnosis system is extremely important for a steam turbine generator set. A novel real-time intelligent fault diagnosis system is proposed by using a fuzzy cerebellar model articulation controller (CMAC) neural network to detect and identify the faults and failures of critical components. A framework of the fault diagnosis system is described. The model of a novel fault diagnosis system by using a fuzzy CMAC is built and analysed in detail step by step. A case of the diagnosis including three faults is simulated with a fuzzy CMAC. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. Moreover, the model is verified by two examples. It is found that this model is feasible. Finally, the effects of the generalization parameter and address number in fault diagnosis are discussed.

Intelligent fault diagnosis system of induction motor based on transient current signal

This paper presents a method for induction motor fault diagnosis based on transient signal using component analysis and support vector machine (SVM). The start-up transient current signal is selected as features source for fault diagnosis. Preprocessing of transient current signal is performed using smoothing and discrete wavelet transform to highlight the salient features of faults. In this work, independent component analysis, principal component analysis and their kernel are performed to reduce the dimension of features and to extract the optimal features for classification process. In this work, the influence of the number of component analysis towards diagnosis accuracy is also studied. SVM multi-class classification using one against all strategy is selected for classification tool due to good generalization properties. Performance of the system is validated by applying the system to induction motor faults diagnosis. According to the result, the system has potential to serve an intelligent fault diagnosis system in real application.

A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system

This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for “mother wavelet function”, “decomposition level of the signals by means of wavelet analysis”, and “number of neurons in hidden layer” resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.

Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process

During roasting in a shaft furnace (used for the deoxidizing roasting of ore), work-situation faults (WSFs) arise as a result of variations in process conditions and off-spec operation. These work-situation faults can be potentially disastrous and can lead to a total collapse of the control system if they are not detected and diagnosed in time. Furthermore, by their very nature they have to be distinguished from the results addressed by existing methods of diagnosis and tolerance control. This paper presents an innovative work-situation fault diagnosis (WSFD) and fault-tolerance control (FTC) strategy for a control system where a combination of neural networks, expert system, and case-based reasoning is used. As such, a system is established that consists of a magnetic tube recovery rate (MTRR) prediction model, a work-situation fault diagnosis unit, and a fault-tolerance controller. The proposed system diagnoses imminent work-situation faults, and then the fault-tolerance controller adjusts the set-points of the control loops. The outputs of the lower-level control system track the modified set-points, which makes the process deviate gradually from work-situation faults with an acceptable product quality. The proposed system has been applied to the shaft-furnace roasting process in the largest minerals processing factory in China and has reduced the frequency of all work-situation faults by more than 50 per cent, with the ratio of furnace operation increased by 2.98 per cent. It has been proven to provide many benefits to the factory.

Development of an expert system for fault diagnosis in scooter engine platform using fuzzy-logic inference

In the present study, a fault diagnosis system using acoustic emission with an adaptive order tracking technique and fuzzy-logic interference for a scooter platform is described. Order tracking of acoustic or vibration signal is a well-known technique that can be used for fault diagnosis of rotating machinery. Unfortunately, most of the conventional order-tracking methods are primarily based on Fourier analysis with the revolution of the machinery. Thus, the frequency smearing effect often arises in some critical conditions. In the present study, the order tracking problem is treated as the tracking of frequency-varying bandpass signals and the order amplitudes can be calculated with high resolution. The order amplitude figures are then used for creating the data bank in the proposed intelligent fault diagnosis system. A fuzzy-logic inference is proposed to develop the diagnostic rules of the data base in the present fault diagnosis system. The experimental works are carried to evaluate the effect of the proposed system for fault diagnosis in a scooter platform under various operation conditions. The experimental results indicated that the proposed expert system is effective for increasing accuracy in fault diagnosis of scooters.

Intelligent system for process supervision and fault diagnosis in dynamic physical systems

In recent years, the increasing complexity of process plants and other engineered systems has extended the scope of interest in control engineering, which was previously focused on the development of controllers for specified performance criteria such as stability and precision. Modern industrial systems require a higher demand of system reliability, safety, and low-cost operation, which in turn call for sophisticated and elegant fault-detection and isolation algorithms. This paper develops an intelligent supervisory coordinator (ISC) for process supervision and fault diagnosis in dynamic physical systems. A qualitative bond graph modeling scheme, integrating artificial-intelligence techniques with control engineering, is used to construct the knowledge base of the ISC. A supervisor provided by the ISC utilizes the knowledge in the knowledge base to classify various system behaviors, coordinates different control tasks (e.g., fault diagnosis), and communicates system states to human operators. The ISC provides a robust semiautonomous system to assist human operators in managing dynamic physical systems. The proposed ISC has been successfully applied to supervise a laboratory-scale servo-tank liquid process rig.

FAULT DIAGNOSIS EXPERT SYSTEM USING NEURAL NETWORKS FOR ROASTING PROCESS

An intelligent fault diagnosis system integrating neural networks, expert system and case-based reasoning technology is established for the roasting process of shaft furnace in hematite ores processing of China. The structure, function, and realization methods of the proposed system are presented. The proposed system has been successfully applied to the roasting process of shaft furnace in the biggest hematite ores processing factory of China. The industrial application shows the effectiveness of proposed system for the fault diagnosis of the roasting process of shaft furnace and its potential future in the complex industrial process.

Application of an Adaptive Neural Network With Symbolic Rule Extraction to Fault Detection and Diagnosis in a Power Generation Plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Hidden Markov model-based process monitoring system

Sensor signals produced in industrial manufacturing processes contain valuable information about the condition of operations. However, extracting the appropriate feature for effective fault diagnosis is difficult. Moreover, the adaptability and flexibility of current fault diagnosis systems are often found wanting in real-world applications. Unfortunately, it is essential to rebuild most fault diagnosis systems when new fault types emerge. This paper presents an intelligent fault diagnosis system based on a hidden Markov model. Introducing the concepts of time marginal energy and frequency marginal energy, the features of which can be acquired by the wavelet packet technique satisfy the requirements for fault diagnosis. By utilizing the best tree principle, this method not only extracts the feature automatically without a priori experience but also compresses the data; both of which ensure a system that is practical for real-time application. The new diagnosis system developed here is efficient and effective, as demonstrated by the model developed and applied to a real-time sheet metal stamping process. Based on tests conducted during two experiments (one based on simple blanking, the other on progressive operations) and related comparisons, the proposed method is substantially more effective than other approaches. In addition, the new method requires that only related models be created for new fault types, which results show are ideal for shop floor applications.

A computer‐based intelligent system for fault diagnosis of an aircraft engine

In this paper, an intelligent knowledge‐based system (KBS) capable of assisting aircraft mechanics and engineers to deal with fault diagnosis of the turbo‐prop aircraft engine is presented. The KBS intelligent jet engine trouble‐shooting system (IJETSS) employs expert knowledge to act in a way similar to that of a human expert in an aircraft maintenance field by using if‐then rule‐based system. The major aim of the KBS of IJETSS is to generate rapid and precise engine fault diagnosis that can simulate the work of experienced aircraft maintenance mechanics and engineers. The developed system can also be useful for the inexperienced aircraft mechanics and engineers and can be used for training module for them.

A web-based intelligent fault diagnosis system for customer service support

In traditional help desk service centres, service engineers provide a world-wide customer support service through the use of long-distance telephone calls. Such a mode of support is found to be inefficient, ineffective and generally results in high costs, long service cycles, and poor quality of service. With the advent of the Internet technology, it is possible to deliver customer service support over the World Wide Web. This paper describes a Web-based intelligent fault diagnosis system, known as WebService, to support customer service over the Web. In the WebService system, a hybrid case-based reasoning (CBR) and artificial neural network (ANN) approach is adopted as the intelligent technique for machine fault diagnosis. Instead of using traditional CBR technique for indexing, retrieval and adaptation, the hybrid CBR–ANN approach integrates ANN with the CBR cycle to extract knowledge from service records of the customer service database and subsequently recall the appropriate service records using this knowledge during the retrieval phase.

Fault diagnosis competitive neural network with prioritized modification rule of connection weights

In this paper, a competitive neural network architecture is used as an intelligent fault diagnosis system to detect the fault sources in different subsystems or elements of a plant or any other device. The prioritized modification rule for connection weights is introduced and four different procedures are studied and compared from the viewpoint of their efficiency. It is shown that the fourth procedure is more convenient for human type decision-making. The output functions of different neurons are considered as the possibility of being fault sources for different units. The system starts from a vague initial state and the connection weights are modified during the learning procedures. The simulation results of different strategies are analyzed and compared. A typical CNC machine is considered as a case study.