Study On Mechanical Fault Diagnosis Based On Spatial Distribution Features Of Sound Field And Its Application

The development of modern industry makes condition monitoring and fault diagnosisof machinery more and more significant, and the traditional fault diagnosis technology isfacing new challenges. Currenly, fault diagnosis is mainly based on measurement andanalysis of vibration signal, whereas vibration sensors can not be installed conveniently onsome equipment or in some conditions, which makes the application of vibration-basedfault diagnosis get limitation. There is close contact between structural vibration and itsradiated noise. Mechanical noise contains abundant state information, and noise signal candiagnose equipment fault like vibration signal. Acoustic-based diagnosis (ABD) has theadvantage of non-contact measurement, not affecting equipment operation, operatingquickly and easily, which can partially substitute for vibration signal as a supplementarymeans for fault diagnosis. The traditional ABD based on single-channel testing andanalysis can only get local acoustic characteristics with the changes of time or frequency. Itis not easy to choose proper measuring points and the acoustic signal is easilycontaminated. ABD has great potential, but when inappropriate measuring points arechosen or local acoustic characteristics are not sensitive to fault pattern, diagnosis resultswill be affected. Especially in coherent condition, acoustic signal from fault sources issubmerged by that from interference sources, the ABD will no longer apply. The reason isthat fault features based on single-channel acoustic signal can not stablely reflect theoperational status of machinary, which can affect diagnosis results to a large extent.Acoustic imaging techniques can get sound signal at the measuring surface through themicrophone array. It can reconstruct sound field distribution of structure surfaces andpredict the radiated sound field by corresponding reconstruction algorithms. The scheme ofapplying overall information of reconstructed sound field can overcome the limitations intraditional ABD, which creates a condition for the development of new ABD technique.Clearly, Acoustic information from entire reconstruction sound field can contain more faultpatter information than acoustic information based on single point. The latter has only oneor a few local points of acoustic information, while the former get the distributioninformation of entire sound field, which can spatially reflect failure pattern hidden in soundfield. Spatial distribution featrues extracted from the reconstructed sound field are surelymore comprehensive and more stable than that extracted from the conventional ABD. Withstrong interference noise, even if a local single-channel acoustic signal is not stable, but, onthe whole, the spatial distribution pattern of sound field is able to maintain good stability.Running machines can generate radiated sound field, and different operating stateshave different patterns of sound field distribution. Therefore, if the radiation sound fielddistribution pattern can be effectively minied from different states, and the features sensitive to patterns can be extracted, the fault diagnosis will be more efficient and reliablecomparing to the traditional ABD based on single channel signal analysis. In view of thisidea, a feature extraction method based on sound field spatial distribution characteristics isproposed, which extractes textural features from acoustic images to reveal distribution lawof sound field for different machinery operating conditions. Based on this thought, a faultdiagnosis scheme based on space distribution characteristics of sound field is proposed.Then, specifically, a fault diagnosis scheme suitable for medium-high frequency analysisbased on far-field beamforming acoustic images is developed, and a fault diagnosis schemesuitable for medium-low frequency analysis based on near-field acoustic holographyacoustic images is developed. Numerical simulation and experimental research confirmthat in different equipment operating conditions, the textural information can reflect thecorresponding space distribution characteristics of sound field and reveal the differentfailure patterns for fault diagnosis. The fault diagnosis method based on sound field spacedistribution characteristics effectively integrates microphone array measurement, acousticimaging, image processing, feature extraction, pattern identification and othermultidisciplinary research. This method can visualize the radiated sound field of machinery,and mine failure patterns underlying sound field. The experimental research on faultdiagnosis for rolling element bearing and gearbox further validates the effectiveness andpracticality of the proposed method, and the research on effect of noise interference furthershows the superiority of the proposed method. This new ABD method expands theapplication scope of acoustic imaging techniques, provides new ideas and options for ABD,and promotes the development of fault diagnosis technology and its application in practice.The research content of this dissertation can be summarized as follows.(1) The progress of machinery fault diagnosis is reviewed firstly, especially for theABD and intelligent diagnosis. The development of acoustic imaging techniques isoutlined, and the commonly used beamforming and near-field acoustic holographytechnique are summarized.(2) Generation mechanism of machinery radiated sound field is analyzed, and thestructural acoustic radiation question is presented mathematically. The basic formulas ofthe plane near-field acoustic holography are deduced. Several problems of selectingparameters in practical application of near-field acoustic holography are summarized. Thebasic principle of beamforming is introduced, and several problems of selecting parametersin beamforming are concluded. Several typical textural analysis methods are investigatedfor extracting spatial distribution characteristics of sound field. Pattern recognition basedon support vector machine is also introduced.(3) Fault feature extraction and fault diagnosis based on spatial distributioninformation of sound field is put forward. Based on this idea, specifically, a fault diagnosis scheme suitable for medium-high frequency analysis based on acoustic imaging byfar-field beamforming is developed, and a fault diagnosis scheme suitable for medium-lowfrequency analysis based on acoustic imaging by near-field acoustic holography isdeveloped. Then, specific research is carried on, repectively.(4) Based on the investigation on acoustic imaging and feature extraction, amechanical fault diagnosis method based on near-field acoustic holography andHist+GLGCM feature extraction is put forward, and the method is applied to rollingelement bearing fault diagnosis and gearbox fault diagnosis. Rolling element bearing faultdiagnosis test-bed and gearbox fault diagnosis test-bed are established for research onmulti-class fault diagnosis, respectively. Diagnosis results are compared to traditional ABD.The effect of acoustic imaging and diagnosis results with noise interference is also studied.(5) Acoustic images at meshing frequency and its side frequencies are studied, andthe effect of choosing different characteristic frequencies is investigated. For applying thefault diagnosis method based on acoustic imaging tequniques, the noticeable problemsabout choosing characteristic frequencies are concluded. Sound field distributions at sidefrequencies are not stable, and not suitable for fault pattern identification.(6) Diagnosis performances of different gray levels and different textural extractionmethods are investigated. The principle of choosing gray level and the performances ofextracting spatial distribution characteristics of sound field for different textural extractionmethods are concluded, and concrete suggestions are provided for practical applicaiton.(7) The application of acoustic imaging techniques in mechanical diagnosis and testsystem is studied. The common platform of fault diagnosis and test system based onacoustic imaging is designed by virtual instrument, and the principle prototype isdeveloped. The application example of gearbodx fault diagnosis shows its effectiveness.