Reseach On Methods Of Machinery Condition Recognition Based On Manifold Learning

Mechanical equipment is a complicated nonlinear system. Vibration signal analysis isone of the common methods for fault diagnosis, as it contains rich information to describethe mechanical running conditions. The signals are measured from the key parts of amachine, and then features are extracted to represent the running status. The widely usedvibration signal processing methods can be divided into: time domain analysis, frequencydomain analysis, time-frequency domain analysis. Due to the system complexity, the signalis always nonlinear and non-gaussian, which makes the fault diagnosis more difficult. It isnecessary to find new fault diagnosis method for complex nonlinear system diagnosis,keeping the accidents from the beginning and reducing the loss.Manifold learning is one of the most popular focuses in pattern recognition, which isto represent the nonlinear data structure by a nonlinear map from the high-dimension spaceto a low-dimension space, and remaining the most useful information in the subspacesimultaneously. Therefore, we can adopt manifold learning to process various featuresextracted from time domain, frequency domain, time-frequency domain or to analysismulti-source sensor signals to recognize the machine running states. However, there arestill some problems when using manifold learning:(1) noise influences the robustness ofmanifold learning;(2) the parameter affects the mapping result;(3) some methods cannotpreserve the most useful information. So, based on the basics of manifold learning, weinvestigated different manifold learning algorithms in machine condition recognition andperformance assessment, with regard to the noise effect. The main research is as follows:(1)It is time-consuming and memory-consuming to de-noise time signal traditionally,which also leads to difficulties in real time diagnosis. Based on the feature analysis, thenoise contained in the vibration data is transferred to the features. Thus, we de-noise thesefeatures directly to enhance the computational efficiency and conserve the memoryrequirements, which is beneficial to the application of manifold learning in machine faultdiagnosis;(2)Since Locally Linear Embedding(LLE) is very sensitive to the numbers of nearestneighbor, which affects the dimension reduction. Based on the analysis of sample neighborselection, we propose a variable k-nearest neighbor locally linear embedding (VKLLE)algorithm to improve the classification and stability. NPE (Neighborhood PreservingEmbedding) is a linear approximation of the LLE, which is developed for out-of-sample problem. In this paper, NPE and SOM (Self-Organizing Map) are combined to assess thebearing degradation performance;(3)The LPP only focus on local neighborhood information, which neglects the otherfurther samples. A novel method named NFDPP (Nearest-Farthest Distantce PreservingProjection) is proposed to explore data structure by considering a sample’s nearestneighbors and farthest samples at the same time. The experiment results of thebearing-defect classification and engine-fault diagnosis validate that the proposed NFDPPapproach achieves the good performance;(4)Because the SR (Spectral Regression) does not take the global structure intoaccount, a novel feature extraction algorithm, called local and global spectralregression(LGSR), is presented for fault feature extraction. Gear and engine faultexperiments results demonstrate that the LGSR can extract identity information formachine defect classification;(5)Based on the research on NFDPP and LGSR, two multi-way data processingalgorithms, denoted as the Multi-NFDPP and Multi-LGSR, are presented for processingmulti-sensor signals. The Gearbox fault detection experiments and bearing degradationassessment indicate that the proposed algorithms can effectively predict the occurrence ofdefect and find the defect location.