Research On Fault Diagnosis Methods For Machinery Based On Morphological Component Analysis And Chirplet Path Pursuit

With the progress of science and technology, modern mechanical equipments aredeveloping toward large-scale, high-speed, continuum and automatization. Hence, toensure the normal operation of mechanical equipments, the condition monitoring andfault diagnosis technology receives more and more recognition. As the core ofcondition monitoring and fault diagnosis technology, signal processing method hasalways been the research focus for domestic and overseas scholars. When a localfailure occurs in a mechanical equipment, the fault information will be contained inits vibration signal. Therefore, how to extract the fault feature information from thevibration signal has become a hotspot in the area of machinery condition monitoringand fault diagnosis.In general, the measured vibration signal is usually a mixture with manyvibration components generated by different vibration sources. It is significant toseparate each component from vibration signal effectively, which can not only rev ealeach source’s vibration mode, but also provide the basis for fast and accurate faultlocating. As a newly proposed signal processing method, the morphologicalcomponent analysis (MCA) method can effectively separate each signal componentwith distinct morphology from a vibration signal by constructing different sparsedictionaries with different forms. On the other hand, the mechanical equipment oftenoperates under changing rotating speed. In such a case, the frequency blurring oftenhappens when the vibration signal is directly analyzed by FFT-based spectrumanalysis. For this reason, the order tracking method is often applied to transformnon-stationary signal into stationary signal in engineering applications, and the key oforder tracking method is how to acquire an exact rotating speed. The chirplet pathpursuit (CPP) algorithm is another newly presented signal processing method. Byadopting the thought of piecewise linear approximation, the CPP algorithm canestimate a precise rotating speed signal from vibration signals. Supported by NationalNatural Science Foundation (No.50875078,51275161), this dissertation takes deepresearches on separating fault component and extracting fault characteristics frommechanical vibration signals by joint application of MCA method and CPP algorithm.The main researches and innovative achievements of this dissertation are asfollows: (1) Due to the limitation of traditional envelop demodulating method, which isnot suitable to extract modulation information from multi-component signals, a newenvelop demodulating method based on MCA is proposed. The vibration signal of agearbox can be decomposed into harmonic component, impulse component and noisecomponent by using MCA, and then the gear local failure can be dia gnosed in termsof the demodulation analysis of harmonic component, simultaneously, according tothe demodulation result of impulse component, the bearing local failure can also bediagnosed. The results, obtained by the analysis of vibration signals of a gearbox withfaulted bearings, show that the proposed method can effectively extract the faultdemodulation information and highlight the fault characteristic as well. The outcomes,acquired by the analysis of vibration signals of a gearbox with compound faultsconsisted of gear local failure and bearing local failure, indicate that the proposedmethod can separate the fault characteristics of gear and bearing effectively.(2) The fault modulation information of a local failure gear has the character oftime-varying, thus, it cannot be directly extracted from the vibration signal by usingcyclostationary demodulating. Aimed at this problem, an order cyclostationarydemodulating approach based on CPP is proposed. Firstly, the rotating speed isestimated from the gearbox vibration signal by using CPP. And then, according to theestimated rotating speed, the angular domain resampling signal is got by even anglesampling the original signal. Finally, the modulation information of gearbox vibrationsignal can be extracted by using cyclostationary demodulating analysis to the angulardomain resampling signal. The CPP algorithm has the advantages of high accuracyand good anti-noise ability, moreover, the cyclostationary demodulating can extractthe cycle fault feature from noisy signal, hence, the proposed approach inherit themerit of both, and is suitable for extracting fault feature from vibration signal of faultgear with variable rotating speed. Simulation and application examples indicate thatthe proposed approach is an effectively way to extract the gear fault characteristicfrom vibration signal of a changing rotating speed gearbox.(3) Aiming at the problem of extracting modulation information from vibrationsignal of a gearbox with compound faults under changing rotate speed, an energyoperator demodulating approach based on CPP is proposed. Firstly, the rotating speedis estimated using CPP algorithm so as to resample the gearbox vibration signal ineven angle. Then, the energy operator demodulating is employed to analyze theangular domain resampling signal. Finally, the compound faults diagnosis of achanging-speed gearbox is implemented in terms of the modulation information in the demodulating spectrum. The vibration signals of a changing-speed gearbox withcompound faults consisted of gear local failure and bearing local failure are analyzedby both simulation and example applications, and the analysis results demonstrate thatthe proposed method can effectively extract fault characteristic from achanging-speed gearbox with compound faults under the condition of lack oftachometer.(4) Aiming at the problem of separating fault characteristics from a changingrotating speed gearbox with compound faults, a compound faults diagnosis methodbased on MCA and CPP is proposed. Firstly, each component with fault information isseparated from the gearbox vibration signal by using MCA; simultaneously, therotating speed is estimated from the gearbox vibration signal by using CPP. Secondly,according to the estimated rotating speed, each component is analyzed by envelopeorder spectrum. Lastly, the compound faults diagnosis are carried out according to theenvelop order spectrum of each component. The results, obtained by the analysis ofsimulation and application examples to the changing-speed gearbox with compoundfaults consisted of gear local failure and bearing local failure, prove that the proposedmethod can effectively separate fault characteristic from a changing rotating speedgearbox with compound faults.(5) In the early stage of gear failure, the fault modulation information isrelatively weak, moreover, due to the geometric shape and assembly errors, thevibration signal of a normal gear can also generate amplitude modulation andfrequency modulation (AM-FM) phenomenon, consequently, the misdiagnosis mayhappens. In view of this problem, a fault diagnosis method for changing-speed gearbased on instantaneous frequency and instantaneous amplitude is proposed, and it isapplied to diagnose the local failure gear with variable rotating speed in terms ofinstantaneous frequency and instantaneous amplitude. The results, acquired by theanalysis of the gear vibration signal under variable rotating speed, exhibit that thehealth condition of a changing rotating speed gear can be effectively identified by theproposed approach.According to the morphological difference of each component in the vibrationsignal, the MCA approach can effectively separate each component from the vibrationsignal by building different sparse dictionaries with different forms. While the CPPmethod can estimate rotating speed signal from the original vibration signal, and hasthe characters of high accuracy and good anti-noise ability. Therefore, thisdissertation combines the MCA and CPP approach and employs them to diagnose mechanical faults. Simulation and application examples indicate that the combinationmethods, which are obtained by combinating MCA and CPP with the demodulatingmethods such as cyclostationary demodulating and energy operator demodulating, canextract the fault feature from the mechanical fault vibration signals effectively, andconsequently have a good engineering application prospect.