Fault diagnosis of bearing based on the kernel principal component analysis and optimized k-nearest neighbour model

Abstract

Aiming to identify the bearing faults level effectively, a new method based on kernel principal component analysis and particle swarm optimization optimized k-nearest neighbour model is proposed. First, the gathered vibration signals are decomposed by time-frequency domain method, i.e., local mean decomposition; as a result, the product functions decomposed from the original signal are derived. Then, the entropy values of the product functions are calculated by Shannon method, which will work as the input features for k-nearest neighbour model. The kernel principal component analysis model is used to reduce the dimension of the features, and then the k-nearest neighbour model which was optimized by the particle swarm optimization method is used to identify the bearing fault levels. Case of test and actually collected signal are analysed. The results validate the effectiveness of the proposed algorithm.

Acoustic-Based Fault Diagnosis of Commutator Motor

“…, where FS-A1, FS-A2, FS-A3, FS-A4-denoted 4 frequency spectra of state A, FS-B1, FS-B2, FS-B3, FS-B4-denoted 4 frequency spectra of state B, FS-C1, FS-C2, FS-C3, FS-C4-denoted 4 frequency spectra of state C, FS-D1, FS-D2, FS-D3, FS-D4-denoted 4 frequency spectra of state D, FS-E1, FS-E2, FS-E3, FS-E4-denoted 4 frequency spectra of state E. Next, 40 differences between frequency spectra are computed: The MSAF-15-MULTIEXPANDED-8-GROUPS found 28 essential frequency components: 48,50,79,81,97,101,128,157,159,1469,1471,1672,1926,1927,1934,1935,1939,1942,1953,1957,1958,1961,1978,2038,2039,2042 Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].…”

“…x -cmbrc (1) where = [x36, x37, x59, x60, x72, x75, x95, x117, x118, x1092, x1094, x1243, x1432, x1433, x1438, x1439, x1442, x1444, x1452, x1455, x1456, x1458, x1471, x1515, x1516, x1518, x1531, x1894] and training feature vector cmbrc = [cmbrc36, cmbrc37, cmbrc59, cmbrc60, cmbrc72, cmbrc75, cmbrc95, Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].…”

“…Distance metrics such as Euclidean, Manhattan, and Minkowski had similar results. In this paper, the classification step was carried out using Manhattan distance (1): [35,36].…”

Intelligent real-time monitoring of Computer Numerical Control tool wear based on a fractional-order chaotic self-synchronization system

“…Many recent studies have focused on the diagnosis of mechanical systems, such as bearings, [3][4][5] AC generators, 6 gearboxes. 7,8 Several different methods have been used for the monitoring and prediction of tool life and wear.…”

Non-model based structural damage assessment using improved analytical redundancy relations

“…[1][2][3] Damage assessment based on structural measurement data is the core of a health monitoring procedure, whose purpose is to figure out whether a structure is damaged and where the damage is. Meanwhile, damage assessment or fault diagnosis is often carried out using different optimization algorithms 4,5 or machine learning approaches. 6 In general, static and dynamic measurements of a structure are adopted as objective responses for damage assessment.…”