Papers by Keyword: Rotating Machinery

Abstract: It is difficult to realize an accurate and reliable diagnosis in the rotating machinery. To solve this problem, a Wavelet Neural Network (WNN) diagnosis model based on EKF algorithm is proposed. In the model, EKF algorithm is introduced to optimize the parameters of WNN, and then the built WNN model is used to diagnose the faults of the rotating machinery. The experiment shows that, the proposed model has a good diagnosis capability in the field of the rotating machinery.

Abstract: To diagnose the fault that occurs in rotating machinery, a neural network diagnosis method based on an improved GA algorithm is proposed. In this diagnosis method, a case injected idea is introduced to improve the strong global search capability of traditional GA algorithm; and then the improved GA algorithm is used to optimize the parameters of neural network, fulfilling the training of neural network. Simulation result indicates that, the proposed diagnosis method has a good practicability in the field of fault diagnosis for rotating machinery.

Abstract: . Aiming at the purification of rotor center’s orbit, a new approach was presented by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a series of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose some interested IMFs and reconstructed the needed signal. By doing this the noises would be eliminated successfully. At last the purification of rotor center’s orbit was obtained by extracting the useful signal component. Simulation and practical results show the advantage of EEMD in noise de-noising and purification of rotor center’s orbit. This method also has simple algorithm and high calculating speed; it provides a new way for purification of rotor center’s orbit of rotating machinery.

Abstract: The fighter aircraft transmission system consists of a light weight, High Speed Flexible Coupling (HSFC), used to transmit power from engine gear box to accessory gear box at speed ranging from 10,000 to 18,000 rpm. The HSFC accommodates larger parallel and axial misalignment resulting from differential thermal expansion of the aircraft engine and mounting arrangement. As the HSFC operates at higher rotational speeds close to critical velocities, it is important to analyze, the unbalance exciting forces considering the misalignment. In the present work, prediction of critical speed by camp bell diagram and unbalance response of the HSFC has been carried out using FEA. An experimental investigation also been carried out to study the influence of applied misalignment on a bi-plane dynamically balanced HSFC. The study shows that lower reaction forces are transmitted to HSFC end supports with the applied misalignments, as they are accommodated by the elastic material flexure of flexible plates.

Abstract: As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.

Abstract: The authors present extreme learning machine (ELM) as a novel mechanism for diagnosing the faults of rotating machinery, which is reflected from the power spectrum of the vibration signals. Extreme learning machine was originally developed for the single-hidden layer feedforward neural network (SLFN) and then extended to the generalized SLFN. We obtained the fault feature table of rotating machinery by wavelet packet analysis of the power spectrum, then trained and diagnosed the fault feature table with extreme learning machine. Diagnostic results show that the extreme learning machine method achieves higher diagnostic accuracy than the probabilistic neural network (PNN) method, exhibiting superior diagnostic performance. In addition, the diagnosis of fault feature table adding noise signal indicates the extreme learning machine method provides satisfactory generalization performance.

Abstract: As failure of rotator in rotating machinery has a certain concealment, fault diagnosis for rotator in rotating machinery based on support vector machine with particle swarm optimization algorithm is presented in the paper. And particle swarm optimization algorithm is applied to select the suitable parameters of support vector machine. In the study, we employ three PSO-SVM classifiers to recognize the four states of rotator in rotating machinery including normal state, rotor imbalance, rotor winding and rotor misalignment. More than 70 cases are used to testify the effectiveness of the PSO and SVM model compared with other classification models. The experimental results show that diagnostic precision for rotating machinery of PSO and SVM than that of SVM and BPNN.

Abstract: The rotating machineries in a factory usually have the characteristics of complex structure and highly automated logic, which generated a large amounts of monitoring data. It is an infeasible task for uses to deal with the massive data and locate fault timely. In this paper, we explore the causality between symptom and fault in the context of fault diagnosis in rotating machinery. We introduce data mining into fault diagnosis and provide a formal definition of causal diagnosis rule based on statistic test. A general framework for diagnosis rule discovery based on causality is provided and a simple implementation is explored with the purpose of providing some enlightenment to the application of causality discovery in fault diagnosis of rotating machinery.

Abstract: This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Abstract: A method of Rotating Machinery fault feature extraction based on wavelet transform and Hilbert demodulation is been studied. On the basis of rotating machinery fault mechanism and spectral characteristics, wavelet transform is used to be decompose the vibration acceleration signals of bearing faults into different frequency bands, Which is then used to achieve accurate fault information by Hilbert demodulation. The result shows the method can effectively improve the frequency resolution and realize accurate extraction of fault feature, and it has certain practical value for industrial production of rotating machinery faults diagnosis when applied to the production industry. Key words: Rotating Machinery; bearings; Wavelet algorithm; Hilbert demodulation