Optimization And Application Of Sparse Decomposition In Rolling Bear Fault Diagnosis

Rolling bears are integral components of the transmission system have importantrole. The condition monitoring and fault diagnosis on the bearing is of greatsignificance to ensure operational safety, prevent serious accidents and reduceproduction cost.This paper focus on the efficiency analysis, optimization and application ofsparse decomposition in bearing fault diagnosis, is divided into several parts：(1) Study the compositions, characters of fault bearing signal and the principle ofbearing fault diagnosis. Than introduce the principle of basic pursuit and matchingpursuit algorithm. Analysis the three essential factors of sparse decomposition anddescription every factor in detail (The establishment of dictionary model, Theimplementation methods and the iteration termination) Feasibility analysis of sparesdecomposition applied to bearing fault diagnosis is carried out combing with bearingfault signal character.(2) A new dictionary model is established according to the characteristics andmechanism of bearing faults based on traditional impulse dictionary. The new modelincorporates the rotational speed of the bearing, dimension of the bearing, and bearingfault status, among other parameters be called New Impulse Dictionary model. Thismodel optimized the accuracy of traditional dictionary. And the comparison ofexperiment and simulation verified the accuracy and effectiveness of New ImpulseDictionary model.(3) Established a matching pursuit algorithm using new impulse dictionarycombining with genetic algorithm. Simulation and experiment suggest that newimpulse dictionary used in matching pursuit algorithm combining with geneticalgorithm have more accurate effect on bearing fault diagnosis than using traditionalimpulse dictionary. But it still has some weak points of poor stability, rapidity andcontrollability by using genetic algorithm.(4) The self-adaptive impulse dictionary is established based on new impulsedictionary. The basic pursuit and matching pursuit algorithm of self-adaptive impulsedictionary is adopted to analyze the simulated experiment and practice signals. Theresults show that basic pursuit and matching pursuit algorithm of self-adaptiveimpulse dictionary have better effect on bearing fault diagnosis than matching pursuitalgorithm combining with genetic algorithm.(5) A fault diagnosis system based on sparse decomposition for bearing fault isdeveloped by using LabVIEW. The system contained several functions of signalacquisition, signal processing and signal storing etc. The structure is reasonable andthe interface is friendly. Provide a feasible scheme for bearing fault diagnosis by using sparse decomposition.