Rolling bearing health status assessment based on ITD-GMM method

This paper proposed a Rolling bearing health state assessment based on ITD-GMM method to fully dig the favorable information of the vibration signal from the rolling bearing with decline trend. By data analytic, the six components of vibration signal were calculated, and each component has three feature vectors. Finally, the performance of rolling bearing was quantified, and the curve of performance was acquired. The experimental results indicate that the method is feasible and effective for the assessment of rolling bearing

Fault diagnosis of gearbox based on the nonlinear output frequency response functions and PNN

In this study, the fault diagnosis of nonlinear gearbox is investigated by using the frequency domain approach. The Nonlinear Output Frequency Response Functions (NOFRFs) of the nonlinear gearbox are evaluated and be used to diagnosis the abnormal conditions of the gears and the bearings. A case study is used to illustrate the NOFRFs based feature extraction method, then a probabilistic neural network (PNN) is mentioned to classify the failure modes. Finally, the results show that this method can complete the fault diagnosis of gearbox perfectly. The study provides a basis of the application of the NOFRFs and PNN in the fault diagnosis or condition monitoring of complex nonlinear systems

Chaotic information-geometric support vector machine and its application to fault diagnosis of hydraulic pumps

Fault diagnosis of rotating machineries is becoming important because of the complexity of modern industrial systems and the increasing demands for quality, cost efficiency, reliability, and safety. In this study, an information-geometric support vector machine used in conjunction with chaos theory (chaotic IG-SVM) is presented and applied to practical fault diagnosis of hydraulic pumps, which are critical components of aircraft. First, the phase-space reconstruction of chaos theory is used to determine the dimensions of input vectors for IG-SVM, which uses information geometry to modify SVM and improves performance in a data-dependent manner without prior knowledge or manual intervention. Chaotic IG-SVM is trained by using the dataset from the normal state without fault, and a residual error generator is then designed to detect failures based on the trained chaotic IG-SVM. Failures can be diagnosed by analyzing residual error. Chaotic IG-SVM can then be used for fault clustering by analyzing residual error. Finally, two case studies are presented, and the performance and effectiveness of the proposed method are validated

Performance Assessment and Fault Diagnosis for Hydraulic Pump Based on WPT and SOM

Hydraulic pump is the heart of hydraulic system, therefore a real-time condition monitoring for hydraulic pump is crucial to the reliability of the entire system. In this study, a method for performance assessment and fault diagnosis to hydraulic pump based on wavelet packet transform (WPT) and Self-organizing mapping (SOM) neural network is proposed. First, WPT is utilized to decompose the vibration signal into components, energy of each component is extracted and normalized to form the feature vector. Second, SOM neural network, trained only by normal data, is used to map feature vectors into Minimum Quantization Error (MQE), which is then normalized into confidence values (CV). Performance assessment is accomplished by tracking the trends of CVs. Finally, when faults occur, SOM, trained by both normal and faulty samples, is employed to classify the faults into different groups, which delegates different fault modes of the hydraulic pump. In addition, Taguchi method is used to reduce the redundant features and extract the principal components to ensure the effectiveness of the approach. A case study based on the vibration dataset of test plunger pump rig is conducted to demonstrate that the proposed method is able to assess the performance of hydraulic pump and diagnose faults suitably

Application of Information-Geometric Support Vector Machine on Fault Diagnosis of Hydraulic Pump

The growing demand for the safety and reliability in industries triggers the development of condition monitoring and fault diagnosis technologies. Hydraulic pump is the critical part of a hydraulic system. The diagnosis of hydraulic pump is very crucial for reliability. This paper presents a method based on information-geometric support vector machine (IG-SVM), which is employed for fault diagnosis of hydraulic pump. The IG-SVM, which uses information geometry to modify SVM, improves the performance in a data dependent way. To diagnose faults of hydraulic pump, a residual error generator is designed based on the IG-SVM. This residual error generator is firstly trained using data from normal state. Then, it can be used for fault clustering by analysis of the residual error. Its feasibility and efficiency has also been validated via a plunger pump test-bed

Health assessment of rotary machinery based on integrated feature selection and Gaussian mixed model

Bearing failure is the most common failure mode of all rotary machinery failures, and can interrupt the production in a plant causing unscheduled downtime and production losses. A bearing failure also has the potential to damage machinery causing soaring machinery repair and/or replacement costs. In order to prevent unexpected bearing failure, a health assessment method is proposed in this paper. It employs an integrated feature selection approach and Gaussian mixture model (GMM). Firstly, the integrated feature selection approach, which combines empirical mode decomposition (EMD), singular value decomposition (SVD) and Principal Component Analysis (PCA), processes nonlinear and non-stationary vibration signals of a bearing and extracts features for health assessment. Then, GMM is utilized to evaluate and track the health degradation of the bearing in terms of confidence values (CV). This method, which is notable for bearing health tracking and detect the defect at its incipient stage, can be used without the need for failure datasets in applications. Finally, the feasibility and efficiency of this method was validated by two datasets of different bearing experiments

An approach to performance assessment and fault diagnosis for hydraulic pumps

The hydraulic pump is the heart of the hydraulic system. Therefore, monitoring the condition of such a pump in real time is crucial to the reliability of the entire system. In this study, a method that assesses the performance of and diagnoses faults in hydraulic pumps is proposed. This method is based on wavelet packet transform (WPT) and a self-organizing mapping (SOM) neural network. First, WPT is used to decomposes the vibration signal into components. The energy of each component is then extracted and normalized to form feature vectors. Second, the SOM neural network, which is trained by normal data only, maps feature vectors into minimum quantization errors, which are then normalized into confidence values (CVs). Performance is assessed by tracking CV trends. Finally, SOM, which is trained by both normal and faulty samples, classifies faults into different groups when they occur. These groups represent the various fault modes of the hydraulic pump. In addition, Taguchi method is employed to reduce the number of redundant features and extract the principal components, thereby ensuring the effectiveness of the approach. A case study based on the vibration dataset of the rig of a test plunger pump is conducted to demonstrate the ability of the proposed method to assess the performance of a hydraulic pump and suitably diagnose faults

An approach to health assessment for tools in milling machine

Tool health is identified as the most significant index of the machining process, which directly influences the surface quality of work-piece. An online health monitoring for tools has become more crucial in manufacturing industries. In this study, a health assessment approach for tools in milling machine is presented. First, the vibration signal of tools is decomposed into a finite number of components called intrinsic mode function (IMF) by the empirical mode decomposition (EMD), which are regarded as the initial feature vector matrices. Second, Singular value decomposition (SVD) is used to extract the singular values of the matrices, which forms the feature vector for health assessment. Third, a Self-organizing mapping (SOM) network is introduced to map the extracted feature vectors into Minimum Quantization Error (MQE), and the Taguchi system is then employed to reduce the redundant features. Finally, the MQE is normalized into a confidence value (CV), representing the health status of the tools. A case study demonstrates that the proposed approach can effectively realize the health assessment for tools in milling machine by monitoring of the vibration signals

A deep learning method using SDA combined with dropout for bearing fault diagnosis

The fault diagnosis of a rolling bearing is at present very important to ensure the steadiness of rotating machinery. According to the non-stationary and non-liner characteristics of bearing vibration signals, a large number of approaches for feature extraction and fault classification have been developed. An effective unsupervised self-learning method is proposed to achieve the complicated fault diagnosis of rolling bearing in this paper, which uses stacked denoising autoencoder (SDA) to learn useful feature representations and improve fault pattern classification robustness by corrupting the input data, meanwhile employs “dropout” to prevent the overfitting of hidden units. Finally the high-level feature representations extracted are set as the inputs of softmax classifier to achieve fault classification. Experiments indicate that the deep learning method of SDA combined with dropout has an advantage in fault diagnosis of bearing, and can be applied widely in future

Detection and localization of closely distributed damages via lamb wave sparse reconstruction

Ultrasonic Lamb wave is a promising tool for structural health monitoring and nondestructive evaluation of plate-like structures. Using an array with several piezoelectric discs for damage imaging (i.e. visual detection and localization) is of interest. Commonly used delay-and-sum method is limited for overlapped signals when several damages are closely distributed in the structure. To overcome this limitation, modal-based sparse reconstruction imaging method is applied for adjacent damages in this study. Firstly, Lamb wave dispersion curve is obtained by solving the Rayleigh-Lamb equations. Subsequently, propagation modal of the damage-reflected signal is constructed based on the solved dispersion curve. Finally, the modal is used for damage imaging via sparse reconstruction and basis pursuit de-noising. Experimental data measured in an aluminum plate is considered, and the result demonstrates that the sparse reconstruction imaging method is effective to detect and localize closely distributed damages in the presence of signal overlapping