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Motor Current Signal Analysis using a Modified Bispectrum for Machine Fault Diagnosis
Fengshou Gu,Yimin Shao,Niaoqin Hu,Bruno Fazenda,Andrew Ball 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when themotor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of currentsignal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results dueto random phase variation of the sideband components in the current signal. A modified bispectrum based on theamplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and highersidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. Inparticular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to theanalysis of stator current for the diagnosis of motor drive faults from downstream driving equipment.
Measurement and Diagnostic of Engine Belt Physical Condition from Acoustic Signals
Bruno Fazenda,Fengshou Gu,Mark Avis,Andrew Ball 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
This paper presents an investigation into the diagnosis of transmission belt condition through acousticmonitoring. A relevant belt model and laser interferometry measurements are used to guide the design and analysis of the acoustic monitoring system. The fault under scrutiny is the development of a loss of tension in the belt which mayoccur due to degradation of belt material. Results show that it is possible to diagnose this kind of fault using acoustic diagnostic techniques. Analysis of acoustic signals reveals changes in the natural frequencies of the belt which arematched by results for laser interferometry.
Acoustic Based Safety Emergency Vehicle Detection for Intelligent Transport Systems
Bruno Fazenda,Hidajat Atmoko,Fengshou Gu,Luyang Guan,Andrew Ball 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
A system has been investigated for the detection of incoming direction of an emergency vehicle. Acoustic detection methods based on a cross microphone array have been implemented. It is shown that source detection based on time delay estimation outperforms sound intensity techniques, although both techniques perform well for the application. The relaying of information to the driver as a warning signal has been investigated through the use of ambisonic technology and a 4 speaker array which is ubiquitous in most modern vehicles. Simulations show that accurate warning information may be relayed to the driver and afford correct action.
Luyang Guan,Yimin Shao,Fengshou Gu,Bruno Fazenda,Andrew Ball 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
In this paper, a new method is proposed by combining ensemble empirical mode decomposition (EEMD) with order tracking techniques to analyse the vibration signals from a two stage helical gearbox. The method improves EEMD results in that it overcomes the potential deficiencies and achieves better order spectrum representation for fault diagnosis. Based on the analysis, a diagnostic feature is designed based on the order spectra of extracted IFMs for detection and separation of gearbox faults. Experimental results show this feature is sensitive to different fault severities and robust to the influences from operating conditions and remote sensor locations.
A Simulation Study of Defects in a Rolling Element Bearing using FEA
Yimin SHAO,Wenbing Tu,Fengshou GU 제어로봇시스템학회 2010 제어로봇시스템학회 국제학술대회 논문집 Vol.2010 No.10
An internal impact usually happens when there is a small defect in one part of rolling bearings. The Fault signal from this impact is always masked by different noises such as strong vibrations from other parts and the random noise of instrumentation, which makes it difficult to extract an accurate feature signal for early fault diagnosis. In this paper, a simulation study is conducted using the method of finite element analysis (FEA) to understand the vibration characteristics from the small impact. The vibration responses have been modelled based on a typical bearing assembly. Common faults including outer ring defect, inner ring defect and rolling ball defect are simulated and their vibration responses are compared between different faults and at different locations in the bearing housing. The results obtained have shown that under the same defect size, the vibration from the outer ring is the highest whereas that from the rolling ball is the smallest. In addition the vibration close to the mounting hole attenuates considerably compared to that close to outer ring. These findings provide fundamental information to place vibration sensors and to analyse vibration signals.
Novelty detection methods for online health monitoring and post data analysis of turbopumps
Hu Lei,Hu Niaoqing,Zhang Xinpeng,Gu Fengshou,Gao Ming 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.7
As novelty detection works when only normal data are available, it is of considerable promise for health monitoring in cases lacking fault samples and prior knowledge. We present two novelty detection methods for health monitoring of turbopumps in large-scale liquidpropellant rocket engines. The first method is the adaptive Gaussian threshold model. This method is designed to monitor the vibration of the turbopumps online because it has minimal computational complexity and is easy for implementation in real time. The second method is the one-class support vector machine (OCSVM) which is developed for post analysis of historical vibration signals. Via post analysis the method not only confirms the online monitoring results but also provides diagnostic results so that faults from sensors are separated from those actually from the turbopumps. Both of these two methods are validated to be efficient for health monitoring of the turbopumps.