Development Of Fault Diagnosis System Research Articles

A simple yet realistic integrated dynamical modeling of an induction motor and one-stage spur gearbox system with broken tooth

The development of accurate, useful, dynamical models of electromechanical systems under fault conditions for industrial applications is crucial for further development of fault diagnosis systems. Models including complex physical phenomenology have been proposed, which could have low practical applicability. In this study, we develop the dynamical model of an electromechanical system composed by an induction motor coupled to a one-stage spur gearbox considering the effects of time-varying mesh stiffness, time-varying viscous damping, transmission error, and backlash. The time-varying mesh stiffness includes faulty conditions according to different loss sizes of broken tooth. Thus, this simple yet realistic model can explain the system dynamics under different fault severity scenarios. Experimental and simulated data are compared to validate the proposed model with different levels of pinion broken tooth under constant load conditions on the gearbox. Proper results were obtained, and we conclude that our model is useful for further analysis oriented to fault diagnosis. The simplicity of the proposed model represents a good approximation of the system behavior in steady state, with less complexity than other models with a higher number of DOF.

Research and Development of Fault Diagnosis Methods for Liquid Rocket Engines

Currently, considerable efforts are being focused on the development of reusable rockets and smart rockets due to the heavy requirements of future next-generation aerospace transportation. Safety, low-launching cost, and repeatability are expected from liquid rocket for fulfilling the big dreams of space transportation, exploration, and travelling. Therefore, research on fault detection of the liquid rocket engines (LRE) is critical for satisfying the above claims. Therefore, a comprehensive survey on the research and development of fault diagnosis systems and methods for the liquid rocket engines is presented. First, development history of liquid rocket engine diagnostic systems is reviewed thoroughly. Then three broad headings of the fault detection approaches of liquid rocket engines are divided through the summary and analysis of the existing methods, including approaches using signal processing, model-driven approach, and approach using artificial intelligence (AI). Then the paper discusses the concrete algorithms according to the classification features of the algorithms. In the end, the future developments of the fault detection approaches are presented, which will mainly pay attention to the reusability and intelligence of the rockets.

Design of Pure Electric Vehicle Training Platform and Development of Fault Diagnosis System

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.

Development of Pure Electric Vehicle Fault Diagnosis System Based on Virtual Reality Fusion Technology

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.

Development of fault diagnosis system for wind power planetary transmission based on labview

Development of fault diagnosis system for wind power planetary transmission based on labview

Development of Fault Diagnosis System Using Principal Component Analysis for Intelligent Operation Support System

In this paper, it is proposed to develop the fault diagnosis system using the principal component analysis (PCA) for the intelligent operation support system that calculates the effect of fault propagation in abnormalities situation and gives appropriate information to plant operators. This proposed system using PCA discriminates a failure of equipment based on process variables. The proposed method deals with process variables in steady condition and only one type warning alarm condition that is occurred by several different failures. A set of process variables on each failure is shown as the points on 2dimensional data space by PCA. This system judges as a failure of the equipment when a set of current process variables is closed to the point of a failure of equipment on the data space. The proposed fault diagnosis system is applied to process on a simulator and is confirm its validity. Chemical plants had become very complex for a lot of instrumentation and control systems. It is difficult for operators to predict the effect of fault and to decide corrective actions. In addition, quick response from operator is demanded as any delay response for abnormalities may expand the damage of chemical plant.� Many sensors are required to monitor the chemical process for safety. It is difficult to detect the fault in the process from too many sensors. Therefore many sensors’ data are reduced to less parameter by a multivariate analysis (Kano et al. 2001, 2004, Wise and Gallagher 1996). These methods detect the fault in the process, but not diagnosis the failure of equipment. Our research is developing “Intelligent Operation Support System”. This system calculates the effect of fault propagation in abnormal situations and gives appropriate information to operators. It will help operators to make quick judgments for safety. This system predicts process variables in the abnormalities situation’s plant using a simulator. The states of all equipment are inputted to a simulator in order to calculate process variables correctly. In this paper, it is proposed to develop the fault diagnosis system using the principal component analysis. This proposed system discriminates a failure of equipment in its early stage. This system is important for the intelligent operation support system. The principal component analysis discriminates a failure of equipment based on process variables from the actual plant. A data set is process variables in steady conditions and in abnormalities condition. These data sets are obtained from a simulator when the warning alarm is turned on after the selected equipment is artificially changed to a failure mode. The proposed method deals with process variables in steady condition and only one type warning alarm condition at a time. One type warning alarm is occurred by several different failures, so process variables in one failure are similar to another failure in one type warning alarm. So it is difficult to distinguish one failure among failures in one type warning alarm when the principal component analysis deals with failures in all warning alarm at a time. And the proposed method deal with selected process variables based on value changes after a failure is occurred. A set of process variables on each failure is shown as the points on 2dimensional data space by the principal component analysis. This system judges as a failure of the

Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

Development of Fault Diagnosis System for Telemetry Equipment Based on the Virtual Instrument Technology

A fault diagnosis system based on the Client/Server mode is developed, which realizes the purpose of diagnosing the Telemetry Equipment placing on the station address through local or IP network. And the fault method uses the fault tree to describe the logic relation between fault nodes. And the fault tree is realized by virtual instrument. The signal acquisition module uses PXI architecture, the interactive user interface is develops under LabVIEW graphical software and the data storage uses SQL server database platform. The developed fault diagnosis system can realize the functions of information management on instrument and fault node, fault detection or location, and device remote control. Now this system is used in test area, which improves the telemetry equipment maintenance rate.

The Development of Fault Diagnosis System of Reciprocating Compressor Based on LabVIEW

The technology of equipment condition monitoring and diagnosis plays an important role in maintenance of equipment running normally. The project, using advanced technique , etc, realize functions of on-line monitoring and data analysis for it can not only acquire signals on different measuring positions in equipment continuously, but also look over current trend and make primary fault diagnosis. This paper presents a data acquisition and fault diagnosis system of reciprocating compressor using LabVIEW as the software development platform and shows fault recognition by perception neural network. It is proven by experience that the virtual signal analyzer has achieved relatively satisfactory results.