Bayesian sequential change diagnosis

Sequential change diagnosis is the joint problem of detection and identification of a sudden and unobservable change in the distribution of a random sequence. In this problem, the common probability law of a sequence of i.i.d. random variables suddenly changes at some disorder time to one of finitely many alternatives. This disorder time marks the start of a new regime, whose fingerprint is the new law of observations. Both the disorder time and the identity of the new regime are unknown and unobservable. The objective is to detect the regime-change as soon as possible, and, at the same time, to determine its identity as accurately as possible. Prompt and correct diagnosis is crucial for quick execution of the most appropriate measures in response to the new regime, as in fault detection and isolation in industrial processes, and target detection and identification in national defense. The problem is formulated in a Bayesian framework. An optimal sequential decision strategy is found, and an accurate numerical scheme is described for its implementation. Geometrical properties of the optimal strategy are illustrated via numerical examples. The traditional problems of Bayesian change-detection and Bayesian sequential multi-hypothesis testing are solved as special cases. In addition, a solution is obtained for the problem of detection and identification of component failure(s) in a system with suspended animation

Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

Computer-Aided System for Wind Turbine Data Analysis

Context: The current work on wind turbine failure detection focuses on researching suitable signal processing algorithms and developing efficient diagnosis algorithms. The laboratory research would involve large and complex data, and it can be a daunting task. Aims: To develop a Computer-Aided system for assisting experts to conduct an efficient laboratory research on wind turbine data analysis. System is expected to provide data visualization, data manipulation, massive data processing and wind turbine failure detection. Method: 50G off-line SCADA data and 4 confident diagnosis algorithms were used in this project. Apart from the instructions from supervisor, this project also gained help from two experts from Engineering Department. Java and Microsoft SQL database were used to develop the system. Results: Data visualization provided 6 different charting solutions and together with robust user interactions. 4 failure diagnosis solutions and data manipulations were provided in the system. In addition, dedicated database server and Matlab API with Java RMI were used to resolve the massive data processing problem. Conclusions: Almost all of the deliverables were completed. Friendly GUI and useful functionalities make user feel more comfortable. The final product does enable experts to conduct an efficient laboratory research. The end of this project also gave some potential extensions of the system