Application of selected classification schemes for fault diagnosis of actuator systems

The paper presents the application of various classification schemes for actuator fault diagnosis in industrial systems. The main objective of this study is to compare either single or meta-classification strategies that can be successfully used as reasoning means in the diagnostic expert system that is realized within the frame of the DISESOR project. The applied research was conducted on the assumption that classic as well as soft computing classification methods would be adopted. The comparison study was carried out within the DAMADICS benchmark problem which provides a popular framework for confronting different approaches in the development of fault diagnosis systems.KeywordsFault DetectionFault DiagnosisBase ClassifierSingle ClassifierRule InductionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Machinery will fail due to complex and tough working conditions. It is necessary to apply reliable monitoring technology to ensure their safe operation. Condition-based maintenance (CBM) has attracted significant interest from the research community in recent years. This paper provides a review on CBM of industrial machineries. Firstly, the development of fault diagnosis systems is introduced systematically. Then, the main types of data in the field of the fault diagnosis are summarized. After that, the commonly used techniques for the signal processing, fault diagnosis, and remaining useful life (RUL) prediction are discussed, and the advantages and disadvantages of these existing techniques are explored for some specific applications. Typical fault diagnosis products developed by corporations and universities are surveyed. Lastly, discussions on current developing situation and possible future trends are in the CBM performed.

Support Vector Machine (SVM) is an important machine learning algorithm widely used for the development of machine fault diagnosis systems. In this work, we use an SVM back-end classifier, with statistical features in time and frequency domain as its input, for the development of a fault diagnosis system for a rotating machine. Our baseline system is evaluated for its speed dependent and speed independent performances. In this paper, we use locality constrained linear coding (LLC) to map the input feature vectors to a higher dimensional linear space, and remove some of the speed specific dimensions to improve the speed independent performance of the fault diagnosis system. We use LLC to do the feature mapping to the higher dimensional space, and select only the k nearest neighbour basis vectors to represent the input feature vector and thus reduce/minimize the effect of speed specific factors from the input feature vector, and thus improve the speed independent performance of the fault diagnosis. We compare the performance of the LLC-SVM system for the time and frequency domain statistical features. The proposed approach has improved the overall classification accuracy by 11.81% absolute for time domain features and 10.53% absolute for frequency domain features compared to the baseline speed independent system.

"En el presente trabajo se presenta un estudio sobre la aplicación de estrategias bioinspiradas para la optimización al diagnóstico de fallos en sistemas industriales. El objetivo principal es establecer una base para el desarrollo de nuevos y viables métodos de diagnóstico de fallos basados en modelos que permitan mejorar las dificultades de los métodos actuales. Estas dificultades están relacionadas, fundamentalmente, con la sensibilidad ante la presencia de fallos y la robustez ante perturbaciones externas. En el estudio se consideraron los algoritmos Evolución Diferencial y Optimización por Colonia de Hormigas. La efectividad de la propuesta es analizada mediante experimentos con el conocido problema de prueba de los dos tanques. Los experimentos consideraron presencia de ruido en la información y fallos incipientes de manera que fuera posible analizar las ventajas de la propuesta en cuanto a diagnóstico robusto y sensible. Los resultados obtenidos indican que el enfoque propuesto y, principalmente, la combinación de los dos algoritmos, caracterizan una metodología prometedora para el diagnóstico de fallos."

At the present stage, pure electric vehicles are developing rapidly, and all aspects of pure electric vehicles have been developed comprehensively. However, there are still some problems in the fault diagnosis system of pure electric vehicles. The fault diagnosis system of pure electric vehicles can not meet the needs of consumers. Therefore, at the current stage, the development of fault diagnosis system for pure electric vehicles is A problem. The appearance of virtual reality fusion technology can help the development of pure electric vehicle fault diagnosis system. This paper mainly discusses how to develop the fault diagnosis system of pure electric vehicle based on virtual reality fusion technology.

Most of the existing artificial neural network models use a significant amount of information for their training. The need for such information could be an inconvenience for its application in fault diagnosis in industrial systems, where the information, due to different factors such as data losses in the data acquisition systems, is scarce or not verified. In this chapter, a diagnostic system based on a Hopfield neural network is proposed to overcome this inconvenience. The proposal is tested using the development and application of methods for the actuator diagnostic in industrial control systems (DAMADICS) benchmark, with successful performance.

This chapter presents the motivation for developing fault diagnosis application in industrial systems. Fault diagnosis methods can be broadly categorized into model-based and data-driven. Model-based strategies are briefly discussed while highlighting the increasing tendency to the use of data-driven methods given the increasing data available from process operations. The classic data-driven fault diagnosis loop is presented and each task is described in detail. A procedure is presented for the systematic design of data driven fault diagnosis methods. Finally, the fault diagnosis problem for multimode processes is briefly discussed.

Utilizing the organic Rankine cycle (ORC) for waste heat recovery is an important energy conversion method. Some faults may occur in the ORC in actual operation, but few studies have focused on the fault detection and diagnosis of the whole ORC system. Fault detection detects whether a fault occurs in the system and fault diagnosis diagnoses where the fault is. This paper investigated a fault detection and diagnosis scheme of the ORC system for waste heat recovery based on machine learning. First, a thermodynamic ORC model was established. Three kinds of faults (expander fault, pump fault, and heat exchanger fault) and three kinds of algorithms [logistic regression, softmax regression, and support vector machines (SVMs)] were described. The data of four major important faults (fouling fault of the evaporator and of the condenser, looseness of the mechanical moving parts in the expander, and blocking of the pump) were generated from the thermodynamic ORC model and used to train the fault detection and diagnosis schemes. To evaluate the accuracy of the fault detection and diagnosis schemes, a set of experimental data was employed to test the schemes. The accuracy scores of fault detection using logistic regression and support vector machines were 77.42% and 96.77%, respectively. The accuracy scores of fault diagnosis using softmax regression and SVM were 91.78% and 94.52%, respectively. The test times of fault diagnosis using softmax regression and SVM were 0.0099 and 0.0085 s, respectively. The results demonstrated that machine learning–based fault detection and diagnosis schemes for the ORC have high accuracy and immediacy. Therefore, the proposed schemes are promising tools for fault detection and diagnosis of the ORC system for waste heat recovery.

Faults in mechanical equipment could cause breakdown of time-critical production systems, which is very expensive in terms of production losses and re-commissioning costs. In cloud manufacturing, the scattered distribution of mechanical equipment and fault diagnosis resources, such as experts and specialist instruments, etc., could hinder the development of fault diagnosis systems. The idea of resource servitisation, aimed at resource sharing and collaboration, will lead fault diagnosis systems toward integration, low cost and high efficiency. This paper focuses on the servitisation of fault diagnosis for mechanical equipment in cloud manufacturing. A new service-oriented fault diagnosis system framework for mechanical equipment is proposed, together with a new servitisation method of fault diagnosis for mechanical equipment. Moreover, enabling technologies, e.g. XML, Web Services Definition Language (WSDL), Axis2, are also analysed. Finally, a prototype system is presented that demonstrates the feasibility and effectiveness of the developed architecture and servitisation method in a cloud manufacturing environment.

The main purpose of the researched pure electric vehicle fault diagnosis system is to be used for electric vehicle fault diagnosis and maintenance. The experimental bench integrates pure electric vehicle fault diagnosis and fault point setting. With more advanced fault diagnosis algorithms, it can not only solve the limitations of pure electric vehicles such as untimely diagnosis of hidden faults, slow fault diagnosis, and low diagnosis accuracy, but also improve the efficiency of fault diagnosis and processing of electric vehicles.

Gears are important element in a variety of industrial applications such as machine tool and gearboxes. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Vibration signal analysis has been widely used in the fault detection of rotation machinery. Fault diagnosis plays an important role in condition monitoring to enhance the machine time. In view of this, the present investigation focused on the development of Fault diagnosis system of gearboxes based on the vibration signatures and Artificial Neural Networks. In the present investigation to generate the vibration signatures an experimental set-up has been fabricated with sensing and measuring equipment. The prominent faults, wear, crack, broken tooth and insufficient lubrication of the gear were practically induced in the present investigation. Vibration signatures of the gearbox were collected by transmitting the motion at constant speed with gears having no fault, without applying any load. By inducing one fault at a time, vibration signatures were collected with different degrees of wear on a gear tooth, a gear with a broken tooth, tooth with crack and with insufficient lubrication. As the vibration data of maximum amplitudes was found to be inseparable, fault diagnosis based on this data was not possible. Five prominent statistical features were extracted based on data pertaining to maximum amplitudes of vibration and used fault diagnosis. Due overlapping of this data, it was decided to use ANN based fault diagnosis system for the present investigation. The set of statistical features were extracted based on data pertaining to maximum amplitudes of vibration and used them as input parameters to the ANN based fault diagnosis system designed. Keyword: Gearbox, Conditioning Monitoring, Acoustic Signals, Wavelet Transform, ANN.

Self-adaptive selection graph pooling based fault diagnosis method under few samples and noisy environment

In this work we present a study on the application of bio-inspired strategies for optimization to Fault Diagnosis in industrial systems. The principal aim is to establish a basis for the development of new and viable model-based Fault Diagnosis Methods which improve some difficulties that the current methods cannot avoid. These difficulties are related with fault sensitivity and robustness to external disturbances. To get start the study, we consider the Differential Evolution and the Ant Colony Optimization algorithms. This application is illustrated using simulation data of the Two Tanks System benchmark. In order to analyze the merits of these algorithms to obtain a diagnosis which needs to be sensitive to faults and robust to external disturbances, some experiments with incipient faults and noisy data have been simulated. The results indicate that the proposed approach, basically the combination of the two algorithms, characterizes a promising methodology for Fault Diagnosis.

Online automatic fault diagnosis in industrial systems is essential for guaranteeing safe, reliable and efficient operations. However, difficulties associated with computational overload, ubiquitous uncertainties and insufficient fault samples hamper the engineering application of intelligent fault diagnosis technology. Geared towards the settlement of these problems, this paper introduces the method of dynamic uncertain causality graph, which is a new attempt to model complex behaviors of real-world systems under uncertainties. The visual representation to causality pathways and self-relied “chaining” inference mechanisms are analyzed. In particular, some solutions are investigated for the diagnostic reasoning algorithm to aim at reducing its computational complexity and improving the robustness to potential losses and imprecisions in observations. To evaluate the effectiveness and performance of this method, experiments are conducted using both synthetic calculation cases and generator faults of a nuclear power plant. The results manifest the high diagnostic accuracy and efficiency, suggesting its practical significance in large-scale industrial applications.

Valves and other actuators may fail and cause economic losses or safety accidents. To ensure the stable operation of a control system, it is necessary to identify the failures of various valves and carry out the corresponding maintenance. Several methods are designed and implemented for valve fault diagnosis in this paper. In particular, a novel fault diagnosis method based on a time-series convolution network (FDM-TSCN) is proposed, which is built on a time-series data feature extracting and convolutional neural network. FDM-TSCN can classify 18 out of 19 types of fault, while many other methods cannot. This algorithm is presented in detail and implemented as a prototype system. Comprehensive simulations are performed on valve fault datasets that are generated by the development and application of methods for actuator fault diagnosis in industrial systems (DAMADICS). The simulation results prove the effectiveness and superiority of the proposed FDM-TSCN method. All of the source codes and related data in the paper are made available, which enables other researchers to verify the work easily and may inspire them to carry out more informed research.