Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

Principle Component Analysis

https://escholarship.org/content/qt4999765n/qt4999765n.pdf?t=psy2c4

Multi-Layer domain adaptation method for rolling bearing fault diagnosis

Effective fault diagnosis of rotating machinery has always been an important issue in real industries. In the recent years, data-driven fault diagnosis methods such as neural networks have been receiving increasing attention due to their great merits of high diagnosis accuracy and easy implementation. However, it is mostly difficult to fully train a deep neural network since gradients in optimization may vanish or explode during back-propagation, which results in deterioration and noticeable variance in model performance. In fault diagnosis researches, larger data sequence of machinery vibration signal containing sufficient information is usually preferred and consequently, deep models with large capacity are generally adopted. In order to improve network training, a residual learning algorithm is proposed in this paper. The proposed architecture significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length. Little prior expertise on fault diagnosis and signal processing is required, that facilitates industrial applications of the proposed method. Experiments on a popular rolling bearing dataset are implemented to validate the proposed method. The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach.

Deep residual learning-based fault diagnosis method for rotating machinery

Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism

An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data

Kullback-Leibler Divergence for fault estimation and isolation : Application to Gamma distributed data

Sensible and reliable incipient fault detection methods are major concerns in industrial processes. The Kullback Leibler Divergence (KLD) has proven to be particularly efficient. However, the performance of the technique is highly dependent on the detection threshold and the Signal to Noise Ratio (SNR). In this paper, we develop an analytical model of the fault detection performances (False Alarm Probability and Miss Detection Probability) based on the KLD including the noisy environment characteristics. Thanks to this model, an optimization procedure is applied to set the optimal fault detection threshold depending on the SNR and the fault severity.

Performances Theoretical Model-Based Optimization for Incipient Fault Detection with KL Divergence

Process-history based methods are very commonly used for fault diagnosis and detection. However their efficiency is closely related to the quality of the measured data. In noisy environments, they usually fail particularly for incipient faults. This paper is an attempt to determine an analytical model allowing to estimate a theoretical threshold for fault detection based on the Fault to Noise Ratio (FNR). This model is developed using the Kullback-Leibler Divergence (KLD). For feature extraction, the used data are previously processed through Principal Component Analysis (PCA). The model is validated with simulated data and the results are so far very encouraging.

Capability evaluation of incipient fault detection in noisy environment: A theoretical Kullback-Leibler Divergence-based approach for diagnosis

Incipient fault diagnosis has become a key issue for reliability and safety of industrial processes. Data-driven methods are effective for feature extraction and feature analysis using multivariate statistical techniques. Beside fault detection, fault estimation is essential for making the appropriate decision (safe stop or fault accommodation). Therefore, in this paper, we have developed an analytical model of the Kullback-Leibler Divergence (KLD) for Gamma distributed data to be used for the fault severity estimation. In the Principal Component Analysis (PCA) framework, the proposed model of the KLD has been analysed and compared to an estimated value of the KLD using the Monte-Carlo estimator. The results show that for incipient faults ( 40dB), the analytical model is accurate enough with a relative error around 10%.

Abdulrahman Youssef

Papers

An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data

Kullback-Leibler Divergence for fault estimation and isolation : Application to Gamma distributed data

Performances Theoretical Model-Based Optimization for Incipient Fault Detection with KL Divergence

Capability evaluation of incipient fault detection in noisy environment: A theoretical Kullback-Leibler Divergence-based approach for diagnosis

Analytical model of the KL divergence for gamma distributed data: Application to fault estimation