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Vibration Fault Diagnosis for Large Scale Steam Turbine Sets

Yu Wenhu,Song bin

Strategic Study of CAE 2001, Volume 3, Issue 1,   Pages 44-50

Abstract:

This paper describes the development of vibration fault diagnosis for steam turbines being used in power plant. The problems of vibration fault diagnosis research work are also pointed out. Importance to knowledge scope of diagnosis and the research of the amplitude and phase transfer characteristic are put forward. System of performance and vibration remote monitoring and diagnosis for large-scale steam turbine sets is introduced. Finally, this paper presents the develop trends of vibration fault diagnosis for steam turbines

Keywords： steam turbine sets     fault diagnosis     vibration     performance diagnosis    

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应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断 Article

俊红 张,昱 刘

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2,   Pages 272-286 doi: 10.1631/FITEE.1500337

Abstract: 针对固有时间尺度分解算法的模态混叠问题和最小二乘支持向量机的参数优化问题，本文提出了一种新的基于完备集合固有时间尺度分解和混合差分进化和粒子群算法优化最小二乘支持向量机的柴油机故障诊断方法。然后，提取了前几阶旋转分量的三类典型的时频特征，包括奇异值、旋转分量能量和能量熵、AR模型参数，作为故障特征。最后，提出了混合差分进化和粒子群算法对最小二乘支持向量机的参数进行优化的方法，并通过将故障特征输入训练好的最小二乘支持向量机模型实现故障诊断。仿真和实验结果表明提出的故障诊断方法可以克服固有时间尺度分解的模态混叠问题，而且能够准确的识别柴油机故障。

Keywords： 柴油机；故障诊断；完备集合固有时间尺度分解；最小二乘支持向量机；混合差分进化和粒子群优化算法    

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Study of Dynamic Fuzzy Inference Mechanism of Fault Diagnosis Expert System for Production Line

Tan Li,Liu Jin,Mei Liting

Strategic Study of CAE 2005, Volume 7, Issue 6,   Pages 57-60

Abstract:

Developing fault diagnosis expert system for production line, the principle and method of structuring fuzzy inference engine are presented in this paper. Moreover, the idea of dynamic fuzzy relation with real time is introduced. And, it is illustrated that this idea is realized by defining a dynamic membership function changing with non-fault-time.

Keywords： fault diagnosis     expert system     fuzzy inference    

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Study on the Wavelet Packet Transform Method for Fault Diagnosis of Five Roller Orientation Clutch

Hu Binliang,Luo Yixin,Xie Ming

Strategic Study of CAE 2005, Volume 7, Issue 6,   Pages 66-68

Abstract:

The theory and method of wavelet packet decomposition and its energy spectrum dealing with the fault of the overrunning clutch are presented in the paper. The characteristic frequency band of the fault can be identified by wavelet packet decomposition and its energy spectrum conveniently. At the same time, quantification analysis is performed. The result has shown that this method is more advantageous and of practical value than traditional Fourier analysis method.

Keywords： fault diagnosis     wavelet packet     energy spectrum     clutch    

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Autonomous fault-diagnosis and decision-making algorithm for determining faulty nodes in distributed wireless networks Article

Adel KHOSRAVI,Yousef SEIFI KAVIAN

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 9,   Pages 885-896 doi: 10.1631/FITEE.1500176

Abstract: In this paper, we address fault-diagnosis agreement (FDA) problems in distributed wireless networks (DWNs) with arbitrary fallible nodes and healthy access points. We propose a new algorithm to reach an agreement among fault-free members about the faulty ones. The algorithm is designed for fully connected DWN and can also be easily adapted to partially connected networks. Our contribution is to reduce the bit complexity of the Byzantine agreement process by detecting the same list of faulty units in all fault-free members. Therefore, the malicious units can be removed from other consensus processes. Also, each healthy unit detects a local list of malicious units, which results in lower packet transmissions in the network. Our proposed algorithm solves FDA problems in 2t+1 rounds of packet transmissions, and the bit complexity in each wireless node is O(nt+1).

Keywords： Fault diagnosis     Decision making     Byzantine agreement     Distributed wireless networks     Consensus    

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Adaptive construction of multiwavelet basis function and its applications for mechanical fault diagnosis

He Zhengjia,Sun Hailiang,Zi Yanyang

Strategic Study of CAE 2011, Volume 13, Issue 10,   Pages 83-92

Abstract:

The faults initiated in operation (i.e. incipient fault) with the obscure symptoms and weak features, are always contaminated by a large amount of background noise. Hence, fault diagnosis and prognosis of incipient faults have been the difficulty and focus of the research field. The paper studied the principle of inner product transform of dynamic signals and basis functions, proposed several construction methods of adaptive multiwavelet basis functions,and improved several multiwavelet denoising methods with neighborhood and local threshold. The typical engineering cases of the equipment of heavy oil catalytic cracking, the continuous casting and rolling mills, the air compressor, the electric locomotive and the transmission device of satellite comunication on ship were studied, and the results showed the effectiveness of enhancement of weak dynamic signals and features extraction of compound faults.

Keywords： mechanical fault diagnosis     principle of inner product transform     adaptive basis function     multiwavelet denoising     fault feature extraction    

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Research on Knowledge-based Method for Satellite Fault Diagnosis and Prediction

Yang Tianshe,Yang Kaizhong,Li Huaizu

Strategic Study of CAE 2003, Volume 5, Issue 6,   Pages 63-67

Abstract:

The fault diagnosis and prediction of satellites is a difficult problem due to the complex structure and the unique of operating environment of satellites as well as the presence of multi-source of satellite faults. Usually, one kind of reasoning model can only diagnose and predict one kind of satellite faults. This paper proposes a new method in which the use of multi-modal reasoning for satellite fault diagnosis and prediction is concerned. The method has been used in the development of the knowledge-based satellite fault diagnosis and recovery system and good results have been achieved.

Keywords： satellite     fault     diagnosis     prediction     multi-modal reasoning    

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Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems None

Santiago RUIZ-ARENAS, Zoltán RUSÁK, Imre HORVÁTH, Ricardo MEJÍ-GUTIERREZ

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 152-175 doi: 10.1631/FITEE.1700277

Abstract:

Malfunction or breakdown of certain mission critical systems (MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

Keywords： Failure indicators     Failure classification     Failure detection and diagnosis     Complex systems    

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The design and implementation of remote intelligent condition monitoring and diagnostic system for wind turbines

Yang Wenguang and Jiang Dongxiang

Strategic Study of CAE 2015, Volume 17, Issue 3,   Pages 24-29

Abstract:

This paper researched the key technology of remote intelligent condition monitoring and diagnostic system for wind turbines, and described the development details of a system. The system, adopted the distributed architecture, consisted of four subsystems, which were the data acquisition subsystem, the real time data storage subsystem, the intelligent monitoring and diagnosis subsystem and the user interface subsystem. The intelligent monitoring and diagnosis subsystem used the knowledge base/inference engine structure. An advanced fuzzy expert system is developed for inference engine, and the vibration fault diagnosis rules for wind turbine is stored in the knowledge base. The effectiveness of the system is verified by diagnosing simulated wind turbine faults.

Keywords： wind turbine     diagnostic system     fuzzy expert system    

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Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks Article

De-long FENG,Ming-qing XIAO,Ying-xi LIU,Hai-fang SONG,Zhao YANG,Ze-wen HU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1287-1304 doi: 10.1631/FITEE.1601365

Abstract: Precise fault diagnosis is an important part of prognostics and health management. It can avoid accidents, extend the service life of the machine, and also reduce maintenance costs. For gas turbine engine fault diagnosis, we cannot install too many sensors in the engine because the operating environment of the engine is harsh and the sensors will not work in high temperature, at high rotation speed, or under high pressure. Thus, there is not enough sensory data from the working engine to diagnose po-tential failures using existing approaches. In this paper, we consider the problem of engine fault diagnosis using finite sensory data under complicated circumstances, and propose deep belief networks based on information entropy, IE-DBNs, for engine fault diagnosis. We first introduce several information entropies and propose joint complexity entropy based on single signal entropy. Second, the deep belief networks (DBNs) is analyzed and a logistic regression layer is added to the output of the DBNs. Then, information entropy is used in fault diagnosis and as the input for the DBNs. Comparison between the proposed IE-DBNs method and state-of-the-art machine learning approaches shows that the IE-DBNs method achieves higher accuracy.

Keywords： Deep belief networks (DBNs)     Fault diagnosis     Information entropy     Engine    

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Application of wavelet scalogram in feature extraction of acoustic emission signal

Xiao Siwen,Liao Chuanjun,Li Xuejun

Strategic Study of CAE 2008, Volume 10, Issue 11,   Pages 69-75

Abstract:

Acoustic emission (AE) signals initiated by mechanical faults or damages is composed of two types of signals: high frequency burst impulse signal and long period quasi-stationary noise signal. Wavelet scalogram has a particular time-frequency localization, which helps it to be well used for describing the time-frequency characteristics of AE signals. By analyzing the characteristics and feature extraction of typical AE signals, the paper applies wavelet scalogram for fault diagnosis based on AE technique, and presents the wavelet scalogram analysis method of AE signal for the first time. By theoretical analysis and simulation, the wavelet basis function and parameter related to the function are defined. So the limitation that best time resolution and frequency resolution of wavelet scalogram cannot get at the same time is overcome effectively. When applying wavelet scalogram for fault diagnosis of rolling bearings based on AE techniques, the results are quite visualized, clear and accurate. Both simulations and experimental research prove that wavelet scalogram can be used for condition monitoring and fault diagnosis based on AE detection well.

Keywords： wavelets scalogram     acoustic emission     feature extraction     fault diagnosis     rolling bearing    

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A Real-time Monitoring Network and Fault Diagnosis Expert System for Compressors and Pumps

Gao Jinji

Strategic Study of CAE 2001, Volume 3, Issue 9,   Pages 41-47

Abstract:

Using modern information technology and artificial intelligence to achieve the condition based maintenance and predictive maintenance is one of the important ways to reduce the production cost in the process industries. The real-time monitoring network and artificial intelligent diagnosis technology for mechanical-electric plant was outlined in this paper. The Ethernet and FDDI based real-time monitoring network developed for compressors and pumps in petrochemical plants was introduced briefly. The black-gray-white gathering diagnosis method was given for the first time on the bases of approach to fault mechanism and distinctive symptoms. The mechanical fault diagnosis expert system based on black-gray-white gathering distinguishing sieve method developed in this work yields satisfactory results in the engineering practice.

Keywords： plant diagnosis engineering     real-time monitoring network     artificial intelligent diagnosis     first reason analysis method     black-gray-white gathering     sieving method    

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Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory Research Article

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 7,   Pages 1110-1126 doi: 10.1631/FITEE.2100255

Abstract: This paper presents a group-based dynamic scheme intended for resistive random-access memory (ReRAM). Traditional static random-access memory, dynamic random-access memory, NAND, and NOR flash memory are limited by their scalability, power, package density, and so forth. Next-generation memory types like ReRAMs are considered to have various advantages such as high package density, non-volatility, scalability, and low power consumption, but has been a problem. Unreliable memory operation is caused by permanent stuck-at faults due to extensive use of write- or memory-intensive workloads. An increased number of stuck-at faults also prematurely limit chip lifetime. Therefore, a cellular automaton (CA) based dynamic stuck-at fault-tolerant design is proposed here to combat unreliable cell functioning and variable cell lifetime issues. A scalable, block-level fault diagnosis and recovery scheme is introduced to ensure readable data despite multi-bit stuck-at faults. The scheme is a novel approach because its goal is to remove all the restrictions on the number and nature of stuck-at faults in general fault conditions. The proposed scheme is based on Wolfram's null boundary and periodic boundary CA theory. Various special classes of CAs are introduced for 100% fault tolerance: (SACAs), (TACAs), and (MACAs). The target micro-architectural unit is designed with optimal space overhead.

Keywords： Resistive memory     Cell reliability     Stuck-at fault diagnosis     Single-length-cycle single-attractor cellular automata     Single-length-cycle two-attractor cellular automata     Single-length-cycle multiple-attractor cellular automata    

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Research on An On-line Tracking Self-learning Algorithm for Fuzzy Basis Function Neural Network

Xu Feiyun,Zhong Binglin,Huang Ren

Strategic Study of CAE 2007, Volume 9, Issue 11,   Pages 48-53

Abstract:

An on-line tracking self-learning algorithm for fuzzy basis function (FBF) neural network classifier is proposed in this paper.  Based on the previous possibility distribution of the clusters,  which is kept within the sample mean and covariance matrix with forgetting factor,  a strategy for constructing the target output of the new training sample set is given.  With the new sample set the FBF network can be trained to track the variable clustering boundary.  Meanwhile,  a recursive algorithm for computing the sample mean and covariance matrix with forgetting factor is also proposed to overcome the difficult of storing the vast old training samples.  The proposed method is used for fault recognition of the rotating machinery,  and the results show that it is feasible and effective.

Keywords： fuzzy basis function     self-learning     fault diagnosis    

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Noise Reduction of Vibration Signal of Cyclic Machine Based on the EMD

Yang Jianwen,Jia Minping,Xu Feiyun,Hu Jianzhong

Strategic Study of CAE 2005, Volume 7, Issue 8,   Pages 66-69

Abstract:

The filtering property of empirical mode decomposition is analyzed in the paper. Aimed at the low signal/noise ratio and non stationary feature of vibration signal of cyclic machine, EMD is introduced to the noise reduction of vibration signal and the useful signal is given prominence efficiently, which offers the more efficient foundation to monitor on line and fault diagnosis of cyclic machine. By the simulation and application, it shows that EMD is very useful in reducing noise and provides new means of vibration signal analyzing.

Keywords： fault diagnosis     empirical mode decomposition     cyclic machine     filter    

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Title Author Date Type Operation

Vibration Fault Diagnosis for Large Scale Steam Turbine Sets

Yu Wenhu,Song bin

Journal Article

应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断

俊红 张,昱 刘

Journal Article

Study of Dynamic Fuzzy Inference Mechanism of Fault Diagnosis Expert System for Production Line

Tan Li,Liu Jin,Mei Liting

Journal Article

Study on the Wavelet Packet Transform Method for Fault Diagnosis of Five Roller Orientation Clutch

Hu Binliang,Luo Yixin,Xie Ming

Journal Article

Autonomous fault-diagnosis and decision-making algorithm for determining faulty nodes in distributed wireless networks

Adel KHOSRAVI,Yousef SEIFI KAVIAN

Journal Article

Adaptive construction of multiwavelet basis function and its applications for mechanical fault diagnosis

He Zhengjia,Sun Hailiang,Zi Yanyang

Journal Article

Research on Knowledge-based Method for Satellite Fault Diagnosis and Prediction

Yang Tianshe,Yang Kaizhong,Li Huaizu

Journal Article

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Santiago RUIZ-ARENAS, Zoltán RUSÁK, Imre HORVÁTH, Ricardo MEJÍ-GUTIERREZ

Journal Article

The design and implementation of remote intelligent condition monitoring and diagnostic system for wind turbines

Yang Wenguang and Jiang Dongxiang

Journal Article

Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks

De-long FENG,Ming-qing XIAO,Ying-xi LIU,Hai-fang SONG,Zhao YANG,Ze-wen HU

Journal Article

Application of wavelet scalogram in feature extraction of acoustic emission signal

Xiao Siwen,Liao Chuanjun,Li Xuejun

Journal Article

A Real-time Monitoring Network and Fault Diagnosis Expert System for Compressors and Pumps

Gao Jinji

Journal Article

Cellular automata based multi-bit stuck-at fault diagnosis for resistive memory

Sutapa SARKAR, Biplab Kumar SIKDAR, Mousumi SAHA

Journal Article

Research on An On-line Tracking Self-learning Algorithm for Fuzzy Basis Function Neural Network

Xu Feiyun,Zhong Binglin,Huang Ren

Journal Article

Noise Reduction of Vibration Signal of Cyclic Machine Based on the EMD

Yang Jianwen,Jia Minping,Xu Feiyun,Hu Jianzhong

Journal Article