In this paper, we propose and implement a decision-level fusion model by combining the informatio... more In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.
2007) Multiagent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Process... more 2007) Multiagent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 21(3):pp. 1285-1299.
Predicting a sequence of future values of a time series using the descriptors observed in the pas... more Predicting a sequence of future values of a time series using the descriptors observed in the past can be regarded as the stand-stone of data-driven machinery prognosis. The purpose of this paper is to develop a novel data-driven machinery prognosis strategy for industry application. First, the collected time-series degradation features are reconstructed based on the theorem of Takens, among which the reconstruction parameters, delay time and embedding dimension are selected by the C-C method and the false nearest neighbor method, respectively. Next, the Dempster-Shafer regression technique is developed to perform the task of time-series prediction. Moreover, the strategy of iterated multi-step-ahead prediction is discussed to keep track with the rapid variation of time-series signals during the data monitoring process in an industrial plant. The proposed scheme is validated using condition monitoring data of a methane compressor to predict the degradation trend. Experimental results show that the proposed methods have a low error rate; hence, it can be regarded as an effective tool for data-driven machinery prognosis applications.
Fault detection and diagnosis Uncertainty LFT-based bond graph modeling Auto-associative kernel r... more Fault detection and diagnosis Uncertainty LFT-based bond graph modeling Auto-associative kernel regression Sequential probability ratio test a b s t r a c t To improve reliability, safety and efficiency, advanced methods of fault detection and diagnosis become increasingly important for many technical fields, especially for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. This paper presents a robust fault detection and diagnostic scheme for a multienergy domain system that integrates a model-based strategy for system fault modeling and a data-driven approach for online anomaly monitoring. The developed scheme uses LFT (linear fractional transformations)-based bond graph for physical parameter uncertainty modeling and fault simulation, and employs AAKR (auto-associative kernel regression)-based empirical estimation followed by SPRT (sequential probability ratio test)-based threshold monitoring to improve the accuracy of fault detection. Moreover, pre-and post-denoising processes are applied to eliminate the cumulative influence of parameter uncertainty and measurement uncertainty. The scheme is demonstrated on the main unit of a locomotive electro-pneumatic brake in a simulated experiment. The results show robust fault detection and diagnostic performance.
In this paper, we propose and implement a decision-level fusion model by combining the informatio... more In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.
2007) Multiagent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Process... more 2007) Multiagent decision fusion for motor fault diagnosis. Mechanical Systems and Signal Processing 21(3):pp. 1285-1299.
Predicting a sequence of future values of a time series using the descriptors observed in the pas... more Predicting a sequence of future values of a time series using the descriptors observed in the past can be regarded as the stand-stone of data-driven machinery prognosis. The purpose of this paper is to develop a novel data-driven machinery prognosis strategy for industry application. First, the collected time-series degradation features are reconstructed based on the theorem of Takens, among which the reconstruction parameters, delay time and embedding dimension are selected by the C-C method and the false nearest neighbor method, respectively. Next, the Dempster-Shafer regression technique is developed to perform the task of time-series prediction. Moreover, the strategy of iterated multi-step-ahead prediction is discussed to keep track with the rapid variation of time-series signals during the data monitoring process in an industrial plant. The proposed scheme is validated using condition monitoring data of a methane compressor to predict the degradation trend. Experimental results show that the proposed methods have a low error rate; hence, it can be regarded as an effective tool for data-driven machinery prognosis applications.
Fault detection and diagnosis Uncertainty LFT-based bond graph modeling Auto-associative kernel r... more Fault detection and diagnosis Uncertainty LFT-based bond graph modeling Auto-associative kernel regression Sequential probability ratio test a b s t r a c t To improve reliability, safety and efficiency, advanced methods of fault detection and diagnosis become increasingly important for many technical fields, especially for safety related complex systems like aircraft, trains, automobiles, power plants and chemical plants. This paper presents a robust fault detection and diagnostic scheme for a multienergy domain system that integrates a model-based strategy for system fault modeling and a data-driven approach for online anomaly monitoring. The developed scheme uses LFT (linear fractional transformations)-based bond graph for physical parameter uncertainty modeling and fault simulation, and employs AAKR (auto-associative kernel regression)-based empirical estimation followed by SPRT (sequential probability ratio test)-based threshold monitoring to improve the accuracy of fault detection. Moreover, pre-and post-denoising processes are applied to eliminate the cumulative influence of parameter uncertainty and measurement uncertainty. The scheme is demonstrated on the main unit of a locomotive electro-pneumatic brake in a simulated experiment. The results show robust fault detection and diagnostic performance.
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