A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps

The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectively. SLFDA has a good effect on samples with intra-class multimodality, and the feature set fused by it has obvious multivariate normal distribution characteristics, which is conducive to the next fusion. Second, our modified intra-class correlation analysis (MICA) is used to fuse two feature sets in the second stage. MICA is a CCA (Canonical correlation analysis) -based method. A new class matrix is used to modify the covariance matrix between two feature sets, which allows MICA to conveniently inherit the discriminating structure while fusing features. Finally, we propose a feature selection algorithm based on kernel local Fisher discriminant analysis (KLFDA) and kernel canonical correlation analysis (KCCA) to select the desired features. This algorithm based on Max-Relevance and Min-Redundancy (mRMR) framework solves the problem that CCA cannot properly evaluate the correlation between features and the class variable, as well as accurately evaluates the correlation among features. Based on the experimental data, the proposed method is compared with several popular methods, and the feature fusion methods used in some previous studies related to the fault diagnosis of rotating machinery are compared with it as well. The results show that the fusion effectiveness of our method is better than other methods, which obtains higher recognition accuracy.

Degradation State Identification for Hydraulic Pumps Based on Multi-scale Ternary Dynamic Analysis, NSGA-II and SVM

Degradation state identification for hydraulic pumps is crucial to ensure system performance. As an important step, feature extraction has always been challenging. The non-stationary and non-Gaussian characteristics of the vibration signal are likely to weaken the performance of traditional features. In this paper, an efficient feature extraction algorithm named multi-scale ternary dynamic analysis (MTDA) is proposed. MTDA reconstructs the phase space based on the given signal and converts each embedding vector into a ternary pattern independently, which enhances its capacity of describing the details of non-stationary signals. State entropy (SE) and state transition entropy (STE) are calculated to estimate the dynamical changes and complexity of each signal sample. The excellent performance of SE and STE in detecting frequency changes, amplitude changes, and the development process of fault is verified with the use of four simulated signals. The proposed multi-scale analysis enables them to provide a more precise estimation of entropy. Furthermore, support vector machine (SVM) and nondominated sorting genetic algorithm II (NSGA-II) are introduced to conduct feature selection and state identification. NSGA-II and SVM can conduct the joint optimization of these two goals. The details of the method proposed in this paper are tested using simulated signals and experimental data, and some studies related to the fault diagnosis of rotating machinery are compared with our method. All the results show that our proposed method has better performance, which obtains higher recognition accuracy and lower feature set dimension.

Fault Feature Extraction and Degradation State Identification for Piezoelectric Ceramics Cracking in Ultrasonic Motor Based on Multi-Scale Morphological Gradient

Piezoelectric ceramics cracking is one of the main faults of the ultrasonic motor. According to the morphological mathematics and information entropy, a method based on multi-scale morphological gradient was proposed for ceramics fault feature extraction and degradation state identification. To solve the problem that traditional multi-scale morphology spectral (MMS) entropy cannot exactly describe the performance degradation of the piezoelectric ceramics, multi-scale morphology gradient difference (MMGD) entropy was proposed to improve the sensitivity to the fault. Furthermore, multi-scale morphology gradient singular (MMGS) entropy was presented to reduce the system noise interference to the useful fault information. The disturbance analysis of temperature, load, and noise for MMGD entropy and MMGS entropy was also given in this paper. Combining the advantages of the above two entropies, a standard degradation mode matrix was built to distinguish the degradation state via the grey correlation analysis. The analysis of actual test samples demonstrated that this method is feasible and effective to extract the fault feature and indicate the degradation of piezoelectric cracking in ultrasonic motor.

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

The cracking of piezoelectric ceramics is the main reason of failure of an ultrasonic motor. Since the fault information is too weak to reflect the condition of piezoelectric ceramics especially in the early degradation stage, a fault feature extraction method based on multiscale morphological spectrum and permutation entropy is proposed. Firstly, a signal retaining the morphological feature under different scales is reconstructed with multiscale morphological spectrum components. Then, the permutation entropy of the reconstructed signal is taken as the fault feature of piezoelectric ceramics. Furthermore, a sensitivity factor is defined to optimize the embedded dimension and delay time of permutation entropy according to double sample Z value analysis. Finally, a matrix composed of the probability distributions, obtained from permutation entropy calculation, is applied for the degradation state identification by means of probability distribution divergence. The analysis of actual test data demonstrates that this method is feasible and effective.

Rolling Bearing Degradation State Identification Based on LPP Optimized by GA

In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition (LCD) and six typical features including relative energy spectrum entropy (LREE), relative singular spectrum entropy (LRSE), two-element multiscale entropy (TMSE), standard deviation (STD), RMS, and root-square amplitude (XR) are extracted and compose the original multidomain feature set. And then, locally preserving projection (LPP) is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.

Spatial Information Entropy and Its Application in the Degradation State Identification of Hydraulic Pump

The degradation state identification is a key step of the condition based maintenance of hydraulic pump. In this paper, spatial information entropy (SIE) as a novel degradation feature of pump is proposed based on the study of permutation entropy (PE) algorithm. The fundamental principle of SIE is introduced and contrasted with PE. Different parameters used in the calculation of SIE are discussed and meaningful conclusion is gained. The results of simulation analysis not only checked the rationality of SIE but also demonstrated the availability and superiority of adopting SIE as the degradation feature. Based on simulation analysis, SIE and PE are united and used as degradation feature vector of pump. FCM algorithm is employed to diagnose the degradation state of pump. The analysis results of practical signal testified the rationality and availability of the proposed method.