HIGHER ORDER SPECTRA AND ITS APPLICATION IN MACHINERY FAULT DIAGNOSIS

The application of higher order spectra to machinery faults diagnosis is studied in this paper.A brief review of bispectra is presented,and more emphasis is placed on the ability of higher order spectra to extract diagnostic information from fault signals.Furthermore,by use of the algorithm of higher order spectra,two kinds of typical mechanical faults are analyzed.Results show that the high order spectra analysis is a more efficient method in machinery diagnosis compared with the FFT based spectral analysis.

Overview of the Importance of Intelligent Approaches on Machinery Faults Diagnosis and Prediction Based on Prognostic and Health Management/Condition-Based Maintenance

Condition monitoring is increasingly used to anticipate and detect failures of industrial machines.Failures of machines can cause high maintenance or replacement costs.If neglected,it may result in catastrophic accidents leading to production shrinkage.The potential failure would negatively affect the profitability of the company,including production shut down,cost of spare parts,cost of labor,damage of reputation,risk of injury to people and the environment.In recent years,condition-based maintenance( CBM) and prognostic and health management( PHM) are developed and formed a strong connection among science,engineering,computer,reliability,communication,management,etc.Computerized maintenance management systems( CMMS) store a lot of data regarding the fault diagnosis and life prediction of the machinery equipment.It's too necessary to uncover useful knowledge from the huge amount of data.It's vital to find the ways to obtain useful and concise information from these data.This information can be of great influence in the decision making of managers.This article is a review of intelligent approaches in machinery faults diagnosis and prediction based on PHM and CBM.

2D-HIDDEN MARKOV MODEL FEATURE EXTRACTION STRATEGY OF ROTATING MACHINERY FAULT DIAGNOSIS

A new feature extraction method based on 2D-hidden Markov model（HMM） is proposed. Meanwhile the time index and frequency index are introduced to represent the new features. The new feature extraction strategy is tested by the experimental data that collected from Bently rotor experiment system. The results show that this methodology is very effective to extract the feature of vibration signals in the rotor speed-up course and can be extended to other non-stationary signal analysis fields in the future.

Vibration-based bearing fault diagnosis of high-speed trains:A literature review

Due to the advantages of comfort and safety,high-speed trains are gradually becoming the mainstream public transport in China.Since the operating speed and mileage of high-speed trains have achieved rapid growth,it is more and more urgent to ensure their reliability and safety.As an important component in the bogies of highspeed trains,the health state of the bearing directly affects the operational safety of the trains.It is therefore necessary to diagnoze the faults of bearings in the bogies of high-speed trains as early as possible.In this paper,the bearing fault diagnostic methods for high-speed trains have been systematically summarized with their challenges and perspectives.First,it briefly introduces the structure of bearings in the bogies as well as the fault characteristic frequencies.Then,a brief review of the research on vibration-based signal processing methods and machine learning methods has been provided.Finally,the challenges and future developments of vibrationbased bearing fault diagnostic methods for high-speed trains have been analyzed.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.