Design Of Fault Diagnosis System Research Articles

Research on Spacecraft Fault Diagnosis and Recovery Architecture

Fault diagnosis and recovery system is designed to monitor on-orbit state of spacecraft and handle exception, which provides spacecraft security and missions succeed. This report studies the design of spacecraft fault diagnosis and recovery system from technology and architecture two respects. Technical system and modules design from top level for spacecraft fault diagnosis and recovery is proposed. And the architecture of comprehensive fault diagnosis system is also proposed, which supports spacecraft to diagnose and recover on-orbit faults.

Design of a progressive fault diagnosis system for hydropower units considering unknown faults

To address the misidentification problem of signals containing unknown faults for hydropower units, a progressive fault diagnosis system is designed. Firstly, in view of the non-stationary and nonlinear vibration signals of hydropower units, the method of complementary ensemble empirical mode decomposition is used to process the normal and fault vibration signal samples, and the intrinsic mode function (IMF) and residual components with different frequencies are obtained. Then the IMF energy moment is calculated and used as the feature vector. Furthermore, a classifier (IMF-K1) is constructed based on the feature vector samples of the normal vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit is faulty; a classifier (IMF-K2) is constructed based on the feature vector samples of the fault vibration signals of hydropower units, fault symptom indicators, and K-means algorithm to determine whether the hydropower unit has the known fault; a classifier (IMF-bidirectional long short-term memory neural network (BiLSTMNN)) is constructed to distinguish the fault type of hydropower units by combining the eigenvector samples of known fault vibration signals, fault symptom indicators, and BiLSTMNN. Finally, a progressive fault diagnosis system for hydropower units is constructed using IMF-K1, IMF-K2, and IMF-BiLSTMNN, and comparative experiments are designed using the sample data from the rotor test bench and actual hydropower unit. The results show that the designed progressive fault diagnosis system has greater effectiveness in mining signal features and high fault diagnosis accuracy.

Design of Equipment Fault Diagnosis System Based on Audio Analysis Technology

In the aspect of state monitoring of hydropower unit, the main technical means is to collect and monitor the data on site by deploying various sensors and cameras in the unit and its affiliated units, relying on computer monitoring system and unit state monitoring system. However, it is difficult to intuitively feel the audio characteristics of the unit when it is running. Therefore, an equipment fault diagnosis system based on audio analysis technology is proposed, which makes use of audio analysis technology to analyze and process the on-site sound online, and provides a new auxiliary monitoring means for the equipment running status. Furthermore, the effectiveness of the system is verified by the operation of a power plant.

Design and Implementation of Fault Diagnosis System for Power Internet of Things Equipment Based on Neural Network

The design and application of the equipment fault diagnosis system have been improved and upgraded, allowing it to effectively detect the equipment’s operation status and promptly eliminate hidden faults, reducing the occurrence of unexpected accidents and improving the safety index of people’s lives. The purpose of this essay is to design and apply neural network (NN) fault diagnosis system model in power Internet of things (IOT) equipment and explore its accuracy and effectiveness. The BP neural network (BPNN) algorithm was used to construct model of a fault monitoring testing of the power IOT equipment. Neural network is an algorithmic mathematical model that imitates the behavioral characteristics of animal neural network and performs distributed parallel information processing. The network parameters were as follows: there were four input layer nodes, seven hidden layer nodes, and five output layer nodes, the training times were 10000, and the allowable error was 0.002. In this paper, we use the IOT to detect model of a fault monitoring testing of power equipment designed in each sample, the success rate is as high as 97.5%, and the designed network structure and network parameters are reasonable. The trained loss is less than 0.001, and the nontraining set samples may be appropriately identified. It is clear that the NN has a high application for power equipment fault diagnosis in the IOT value.

Analysis and System Design of Mechanical Fault Diagnosis Based on Deep Neural Network

The operating environment of mechanical equipment is complex, and it is in high-intensity working conditions for a long time. The condition monitoring and fault diagnosis of equipment are very important. As a kind of precision part commonly used in mechanical equipment, the healthy operation of the rolling bearing is a necessary condition to ensure the reliable operation of the whole equipment. This research takes rolling bearing as the research object and is devoted to mechanical fault diagnosis analysis and system design. This paper studies the structure and working principle of rolling bearing, analyzes the types and locations of rolling bearing faults, and puts forward the overall framework of the fault diagnosis system and the workflow of the fault diagnosis system. This paper also studies the relevant theory of deep learning, proposes a neural network model framework for rolling bearing fault diagnosis, and preprocesses the rolling bearing vibration data. It uses a dropout algorithm and GRU to reduce the model parameter size and reduce the risk of overfitting.

Design of satellite ground fault diagnosis system based on rule base

The automation and intelligentization of fault diagnosis system for satellite ground system directly affect the success of tasks and the reliability of the system. This paper designed a fault diagnosis system based on rule base, which contained satellite ground system failure rule base, failure model, abnormal and alarm mechanism. Software implementation has been verified by actual project, it shows that the fault diagnosis system based on rule base can improve the capacity of fault management functions, real-time monitoring and automatic fault diagnosis support system. In addition, fault analysis and location can enhance the automation level and efficiency of satellite fault diagnosis, make efficient and reliable diagnosis of remote sensing satellite receiving system, raise the success rate of satellite data receiving, and have good practicability and popularization.

Intelligent fault diagnosis system design and implementation of diaphragm pump based on artificial intelligence

This article designs and implements a fault diagnosis system for diaphragm pump. The key components of the diaphragm pump are equipped with three-axis vibration intelligent sensor to collect vibration data, the edge side completes the data pre-processing, and then transmits it to the cloud platform through NBIoT. For the non-linear, non-smooth complex system, the time frequency representation is obtained by continuous wavelet transformation, after compressing the time frequency representation as the input of the two-dimensional neural network model, using the two-dimensional convolutional neural network model for training and monitoring, to complete the identification and positioning of diaphragm pump fault.

Design of Motor Intelligent Monitoring and Fault Diagnosis System Based on LoRa

Aiming at the characteristics of low intelligence of the current motor cluster and superconducting electrical equipment monitoring system, high construction and maintenance costs of wired transmission methods. The wireless sensor network constructed by LoRa technology is applied to the motor operating state monitoring system, while the data transmission efficiency of the system is improved by optimizing the topology of the wireless sensor network (WSN) and processing the transmission data redundancy. The system uses STM32F407ZET6 as the main controller, builds an offline operating state library, and uses HMM model for training to achieve real-time acquisition and fault diagnosis of the status information of the cluster system motor's voltage, current, speed, and position. Practical application shows that the system is running well and can realize the acquisition of the motor's running status and fault diagnosis.

Design of sound monitoring and fault diagnosis system for dry-type transformers

This paper focuses on several common fault phenomena and possible causes of dry-type transformer. MATLAB programming realizes the processing of acoustic signal. Through the encapsulation of GUI interface program and the processing and analysis of a large number of fault signals, fault sets are formed. Through the actual transformer fault acoustic signal processing, the main fault frequency of the acoustic signal is extracted and used as an important criterion for transformer fault detection.

Design of Fault Diagnosis and Monitoring System for Electric Drive of Pure Electric Vehicle Based on Computer

Electric vehicle has become a popular product since its emergence. It has both the advantages of simple operation and the characteristics of energy saving and environmental protection in macro development. However, the history of pure electric vehicles is very short, so the electric drive system needs to be further developed and improved. In order to ensure the timely detection of the faults of the electric drive system and meet the needs of driving safety. The author designs the fault diagnosis monitoring system with the convenience of the current computer development.

Research on Testability Design and Evaluation Method for Ship Electromechanical Equipment

As a relatively large amount of electromechanical equipment was installed in the ship systems, it was difficult to position and eliminate the faults of the M&E equipment if any. Hence, there was a need to improve the fault detection and isolation capabilities of the equipment depending on testability design and to develop the corresponding fault diagnosis system when necessary, thus improving the overall availability of the equipment and ensuring that the ship could successfully complete the missions. In view of the above requirements, this paper, taking the most representative M&E equipment - pump set on the ship as a research object, gave the testability design process of the M&E equipment - pump set for product development, illustrated the equipment testability design methods from 4 aspects (namely determination of equipment testability and fault diagnosis system requirements, testability analysis & design, comprehensive fault diagnosis system design and testability verification process design), specified the testability design & monitoring object selection principles and fault characteristic determination methods, and finally provided the testability design evaluation methods for the M&E equipment for verifying whether the testability requirements were met.

Design and Implementation of Intelligent Fault Diagnosis System for Construction Machinery Supporting Wireless Communication Network

Design and Implementation of Intelligent Fault Diagnosis System for Construction Machinery Supporting Wireless Communication Network

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).

Design of an Integrated System for On-line Test and Diagnosis of Rotary Actuators

In this paper, the design of an on-chip Fault Detection and Diagnosis System for Condition Based Maintenance of electromechanical actuators is presented. The proposed system is based on signal processing algorithms integrated in a customized Application Specific Integrated Circuit (ASIC). The design was synthesized using a 90nm CMOS standard cell library. As a case study, post-synthesis simulations were performed using signals acquired from a real electromechanical valve, using torque and vibration sensors considering both fault-free and defective situations for the actuator. Results show the effectiveness of the system in performing real-time fault detection and identification, with low power consumption and low silicon area utilization.

Design of Fast Fault Diagnosis System for Transformer Equipment based on CBR and RBR

After studying the common defects and faults of transformers, we design and propose a fast fault diagnosis system for transformer equipment based on CBR(Case-Based Reasoning) and RBR(Rule-Based Reasoning). In the system, test cases are firstly matched with the cases. If there are similar cases, CBR is used to obtain the diagnosis conclusion. If there are no similar cases, the rule reasoning module based on FTA (Fault Tree Analysis) is launched. This module subdivides transformer fault modes into winding, core, bushing, on-load tap changer, cooling system, non-electric protection and other fault modes, and forms rules. Based on this situation, a transformer equipment fault tree is established to serve the rapid diagnosis system. The structural characteristics and fault modes of transformer windings, iron cores, bushings, on-load tap changers, cooling systems, non-electrical protection and other equipment are subdivided. And then, a fast fault diagnosis model and system for transformer equipment are established. Finally, the case study proved that the system can diagnose and analyse the equipment faults based on the comprehensive multi-source data of transformers, and the diagnosis conclusion is reliable and practical, which has provided support for realizing the state maintenance of transformers.

Design of Fault Diagnosis System Based on Fuzzy Inference for a New Type Equipment

Aimed at the puzzle of non-bijection between cause and symptom of the fault for a new type equipment system, the paper built the mathematical model of fuzzy inference by means of inference mecha- nism of fuzzy relation. In the paper, it designed the overall scheme of fault diagnosis system, and realized the function of system fault diagnosis by means of VC + + programming language according to the diagnosis flow of system. Through actual example validation, it shows that it satisfies the accuracy of fault diagnosis for a new type equipment system. The fault diagnosis of weapon equipment has always been an important topic for military research. With the change of war mode, higher requirements have been put forward for weapon equipment support technology. A new type of equipment is widely used in the land war, so some faults also appear one after another. In the long-term research, it is found that there is a fuzzy relationship between these fault phenomena and the causes. A fault does not correspond to a fault cause in modern mathematics, fuzzy mathematics can clarify the relationship between fault symptoms and fault causes in the equipment system. In this paper, the fuzzy fault diagnosis method is used to build a general fault diagnosis system to realize the fast and accurate analysis and diagnosis of faults.

Optimized Design of Parity Relation-Based Residual Generator for Fault Detection: Data-Driven Approaches

In the conventional approaches to the design of fault diagnosis systems, little effort is usually paid to the selection of the parity vectors. As a result, the systems' performance can be significantly affected. In this article, novel approaches are proposed to derive the parity vectors that construct optimized residual generators for linear and nonlinear systems. Based on the analysis on the parity space dimension, a novel parameterization of all parity relation-based residual generators is proposed. An iterative procedure that guarantees minimal regression error is then employed in the search for the optimal parameters. Considering that the traditional parity relation-based approaches are only suitable for linear systems, in this work, the proposed approach is also generalized to deal with strong nonlinearities, with the aid of data-driven Hammerstein function estimation. Furthermore, optimized residual generation algorithms are summarized for offline design and online implementation, the performance of which is evaluated thoroughly with a three-tank system, a numerical nonlinear example, as well as a case study on an industrial hot rolling mill process. Results show that residuals generated by the proposed approaches can significantly improve the sensitivity to small faults, and thus, the fault detection rate is improved compared with the traditional nonoptimized approach.

Data-driven fault identifiability analysis for discrete-time dynamic systems

Fault identifiability plays a key role in the fault diagnosability performance analysis of a system. However, considering the need for an accurate model, the design of control systems and fault diagnosis systems based on fault identifiability performance suffers from a limited application range because accurate models are rarely available for complex processes or plants, whereas substantial quantities of offline and online data can be obtained from systems with ease. This situation constitutes the motivation to develop a data-driven analysis method for determining the identifiability of faults in dynamic systems. The fault identifiability analysis problem is reformulated as the quantification of the difficulty associated with estimating an unknown value from a regression analysis perspective. Moreover, the proposed identification scheme for a stable kernel representation provides a direct way to identify the matrices required to compute the proposed measure, the estimability, which effectively reduces engineering costs in practical applications. Finally, the proposed method is applied to a vehicle lateral dynamic system to exemplify how to analyse the fault identifiability performance of a dynamic system.

Actuator Fault Diagnosis System Design and Verification for Ball on Plate System

Actuator Fault Diagnosis System Design and Verification for Ball on Plate System

Design of online fault diagnosis and early warning system for coal mine rotating machinery

Design of online fault diagnosis and early warning system for coal mine rotating machinery