Data Mining - Concepts and Techniques.

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A User’s Guide

Artificial intelligence for fault diagnosis of rotating machinery: A review

Applications of machine learning to machine fault diagnosis: A review and roadmap

Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm motivated by intelligent collective behavior of some animals such as flocks of birds or schools of fish. Since presented in 1995, it has experienced a multitude of enhancements. As researchers have learned about the technique, they derived new versions aiming to different demands, developed new applications in a host of areas, published theoretical studies of the effects of the various parameters and proposed many variants of the algorithm. This paper introduces its origin and background and carries out the theory analysis of the PSO. Then, we analyze its present situation of research and application in algorithm structure, parameter selection, topology structure, discrete PSO algorithm and parallel PSO algorithm, multi-objective optimization PSO and its engineering applications. Finally, the existing problems are analyzed and future research directions are presented.

Particle swarm optimization algorithm: an overview

A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

As the rolling bearings being the key part of rotary machine, its healthy condition is quite important for safety production. Fault diagnosis of rolling bearing has been research focus for the sake of improving the economic efficiency and guaranteeing the operation security. However, the collected signals are mixed with ambient noise during the operation of rotary machine, which brings great challenge to the exact diagnosis results. Using signals collected from multiple sensors can avoid the loss of local information and extract more helpful characteristics. Recurrent Neural Networks (RNN) is a type of artificial neural network which can deal with multiple time sequence data. The capacity of RNN has been proved outstanding for catching time relevance about time sequence data. This paper proposed a novel method for bearing fault diagnosis with RNN in the form of an autoencoder. In this approach, multiple vibration value of the rolling bearings of the next period are predicted from the previous period by means of Gated Recurrent Unit (GRU)-based denoising autoencoder. These GRU-based non-linear predictive denoising autoencoders (GRU-NP-DAEs) are trained with strong generalization ability for each different fault pattern. Then for the given input data, the reconstruction errors between the next period data and the output data generated by different GRU-NP-DAEs are used to detect anomalous conditions and classify fault type. Classic rotating machinery datasets have been employed to testify the effectiveness of the proposed diagnosis method and its preponderance over some state-of-the-art methods. The experiment results indicate that the proposed method achieves satisfactory performance with strong robustness and high classification accuracy.

Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

On the basis of the disaster system theory and comprehensive analysis of flood risk factors, including the hazard of the disaster-inducing factors and disaster-breeding environment, as well as the vulnerability of the hazards-bearing bodies, the primary risk assessment index system of flood diversion district as well as its assessment standards were established. Then, a new model for comprehensive flood risk assessment was put forward in this paper based on set pair analysis (SPA) and variable fuzzy sets (VFS) theory, named set pair analysis-variable fuzzy sets model (SPA-VFS), which determines the relative membership degree function of VFS by using SPA method and has the advantages of intuitionist course, simple calculation and good generality application. Moreover, the analytic hierarchy process (AHP) was combined with trapezoidal fuzzy numbers to calculate the weights of assessment indices, thus the weights for flood hazard and flood vulnerability were determined by the fuzzy AHP procedure, respectively. Then SPA-VFS were applied to calculate the flood hazard grades and flood vulnerability grades with rank feature value equation and the confidence criterion, respectively. Under the natural disasters risk expression recommended by the Humanitarian Affairs Department of United Nations, flood risk grades were achieved from the flood hazard grades and flood vulnerability grades with risk grade classification matrix, where flood hazard, flood vulnerability and flood risk were all classified into five grades as very low, low, medium, high and very high. Consequently, integrated flood risk maps could be carried out for flood risk management and decision-making. Finally, SPA-VFS and fuzzy AHP were employed for comprehensive flood risk assessment of Jingjiang flood diversion district in China, and the computational results demonstrate that SPA-VFS is reasonable, reliable and applicable, thus has bright prospects of application for comprehensive flood risk assessment, and moreover has potential to be applicable to comprehensive risk assessment of other natural disasters with no much modification.

Jianzhong Zhou

Papers

A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM

Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders.

Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP

Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm

Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines