Research on unbalance vibration signal de-noising of motorized spindle

Using the adaptive redundant lifting wavelet to the vibration signal de-noising has better de-noising effect, but the traditional threshold function of the method has the problems of discontinuous wavelet coefficients or constant deviation. In order to meet the high precision demand of the active balancing of the motorized spindle and improve the extraction accuracy of the unbalance signal, the improved bivariate threshold function was introduced into the method, and then a new de-noising method on unbalance vibration signal of the motorized spindle based on improving adaptive redundant lifting wavelet was put forward. The new method was applied to the engineering unbalance vibration signal. The result showed that the new method can retain the original signal feature of amplitude and phase, as well as eliminate noise more effectively, when the actual vibration signal of motorized spindle is low SNR and non-stationary

Current-Based Detection of Mechanical Unbalance in an Induction Machine Using Spectral Kurtosis with Reference

This article explores the design, on-line, of an electrical machine’s healthy reference by means of statistical tools. The definition of a healthy reference enables the computation of normalized fault indicators whose value is independent of the system’s characteristics. This is a great advantage when diagnosing a broad range of systems with different power, coupling, inertia, load, etc. In this paper, an original method called spectral kurtosis with reference is presented in order to designa system’s healthy reference. Its principle is first explained on asynthetic signal. This approach is then evaluated for mechanicalunbalance detection in an induction machine using the stator currents instantaneous frequency. The normalized behaviour ofthe proposed indicator is then confirmed for different operatingconditions and its robustness with respect to load variationsis demonstrated. Finally, the advantages of using a statisticalindicator based on a healthy reference compared to a raw faultsignature are discussed

Vibration fault detection and classifaction based on the fft and fuzzy logic

Vibration fault exhibit a multifaceted and nonlinear behavior generation in rotated machines, for example in a steam turbine (ST). Vibration fault (VF) is collectedin the form of acceleration, velocity, and displacement via the vibration sensor. This fault damages the turbines if it strays into the danger zone. This paper first models the VF in a time domain to transfer the frequency domain via an FFT technique. The signals were applied to the fuzzy system to be used by the VF for classification via sugeno and mamdani Fuzzy Inference System (FIS) to generate the signal that will reflect the VF in the event it is embedded into the protection system. The Membership Function (MF) sets depends on practical work in a power plant, and the ISO is interested in ST vibration zones. The outcomes of the sugeno fuzzy property is the generation of stable and usable signals that can be used within the protection system, mostly owing to its efficiency in detecting vibrational faults. The results from this work can be utilized to prevent VF from generating on ST via increased processing that will feed signals for ST controls

A hybrid training method for ANNs and its application in multi faults diagnosis of rolling bearing

A hybrid training method with probabilistic adaptive strategy for feedforward artificial neural network was proposed and applied to the problem of multi faults diagnosis of rolling bearing. The traditional training method such as LM shows fast convergence speed, but it’s easy to fall into local minimum. The heuristic method such as DE shows good global continuous optimization ability, but its convergence speed is slow. A hybrid training method of LM and DE is presented, and it overcomes the defects by using the advantages of each other. Probabilistic adaptive strategy which could save the time in some situation is adopted. Finally, this method is applied to the problem of rolling bearing faults diagnosis, and compares to other methods. The results show that, high correct classification rate were achieved by LM, and hybrid training methods still continued to converge while traditional method such as LM stopped the convergence. The probabilistic adaptive strategy strengthened the convergence ability of hybrid method in the latter progress, and achieved higher correct rate

Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy

The complex dynamic working conditions of wind turbine make it a challenge to identify work status and fault type of wind turbine gearbox. In this paper, a novel method is presented to decompose non-stationary vibration signal and identify wind turbine faults applying ensemble intrinsic time-scale decomposition (EITD) with Wigner bi-spectrum entropy (WBE). Ensemble intrinsic time-scale decomposition (EITD) is able to restrict the end effect and to prevent the signal distortion. Wigner bi-spectrum entropy (WBE) has perfect energy aggregation and can extract the signal feature effectively. The advantage of method is that it does extract the fault features and recognize the gearbox fault types when two or more fault features are close to each other. This proposed approach based on EITD and WBE is applied in the fault diagnosis of wind turbine gearbox

Fault feature extraction for rolling element bearings based on multi-scale morphological filter and frequency-weighted energy operator

In order to extract impulse components from bearing vibration signals with strong background noise, a fault feature extraction method based on multi-scale average combination difference morphological filter and Frequency-Weighted Energy Operator is proposed in this paper. The average combination difference morphological filter (ACDIF) is used to enhance the positive and negative impulse components in the signal. The double-dot structure element (SE) is used instead of zero amplitude flat SE to improve the effectiveness of fault feature extraction. The weight coefficients of the filtered results at different scales in multi-scale ACDIF are adaptively determined by an optimization algorithm called hybrid particle swarm optimizer with sine cosine acceleration coefficients (H-PSO-SCAC). At last, as the Frequency-Weighted Energy Operator (FWEO) outperforms the enveloping method in detecting impulse components of signals, the filtered signal is processed by FWEO to extract the fault features of bearings. Results on simulation and experimental bearing vibration signals show that the proposed method can effectively suppress noise and extract the fault features from bearing vibration signals

A Low Complexity Rolling Bearing Diagnosis Technique Based on Machine Learning and Smart Preprocessing

In this work, we present a diagnosis system for rolling bearings that leverages simultaneous measurements of vibrations and machine rotation speed. Our approach combines the robustness of simple time domain methods for fault detection with the potential of machine learning techniques for fault location. This research is based on a neural network classifier, which exploits a simple and novel preprocessing algorithm specifically designed for minimizing the dependency of the classifier performance on the machine working conditions, on the bearing model and on the acquisition system set-up. The overall diagnosis system is based on light algorithms with reduced complexity and hardware resource demand and is designed to be deployed in embedded electronics. The fault diagnosis system was trained using emulated data, exploiting an ad-hoc test bench thus avoiding the problem of generating enough data, achieving an overall classifier accuracy larger than 98%. Its noteworthy ability to generalize was proven by using data emulating different working conditions and acquisition set-ups and noise levels, obtaining in all the cases accuracies greater than 97%, thereby proving in this way that the proposed system can be applied in a wide spectrum of different applications. Finally, real data from an on-line database containing vibration signals obtained in a completely different scenario are used to demonstrate the distinctive capability of the proposed system to generalize