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Introduction To Robust Estimation And Hypothesis Testing

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

https://hal.archives-ouvertes.fr/hal-01714325/document

Fast computation of the spectral correlation

Gearbox condition monitoring in wind turbines: A review

With the ultimate goal of rotating machinery diagnosis using Instantaneous Angular Speed (IAS) and Time of Arrival (TOA) signals, this paper provides the theoretical background of non-stationary processes existing in the aero-engines and their monitoring using atypical encoders (e.g. fans, compressors and turbine blades cooperating with the induction sensors, AC and DC generators). The model of TOA signal including aperiodic, periodic and stochastics components has been described. The classical and expert approach to monitoring of operational and structural health parameters (CM, NDT, SHM) of aircraft and its power transmission system has been also described. Finally, the experience of the Tip Timing method used in the Armed Forces of the Republic of Poland is presented. The possibility of structural health monitoring and active controlling of the material fatigue by the aero-engine user through interference in the fuel system adjustment quality has been confirmed. Phase portraits have been used to analyze TOA components in transient state of the engine. Diagnostic criteria and expert algorithms have been verified during active and passive experiments.

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Advances in Condition Monitoring of Machinery in Non-stationary Operations

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Condition Monitoring of Machinery in Non-Stationary Operations

Cyclostationarity: theory and methods

Part 1. Signal Processing -- Part 2. Data Mining -- Part 3. Condition Monitoring Techniques.

Advances in Condition Monitoring of Machinery in Non-Stationary Operations Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17

Fakher Chaari

Papers

Advances in Condition Monitoring of Machinery in Non-stationary Operations

Condition Monitoring of Machinery in Non-Stationary Operations

Cyclostationarity: theory and methods

Advances in Condition Monitoring of Machinery in Non-Stationary Operations Proceedings of the Fourth International Conference on Condition Monitoring of Machinery in Non-Stationary Operations, CMMNO'2014, Lyon, France December 15-17

Advances in Acoustics and Vibration