Pitting damage levels estimation for planetary gear sets based on model simulation and grey relational analysis

The planetary gearbox is a critical mechanism in helicopter transmission systems. Tooth failures in planetary gear sets will cause great risk to helicopter operations. A gear pitting damage level estimation methodology has been devised in this paper by integrating a physical model for simulation signal generation, a three-step statistic algorithm for feature selection and damage level estimation for grey relational analysis. The proposed method was calibrated firstly with fault seeded test data and then validated with the data of other tests from a planetary gear set. The estimation results of test data coincide with the actual test records, showing the effectiveness and accuracy of the method in providing a novel way to model based methods and feature selection and weighting methods for more accurate health monitoring and condition prediction

Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition

In this paper, a new method is proposed by combining ensemble empirical mode decomposition (EEMD) with order tracking techniques to analyse the vibration signals from a two stage helical gearbox. The method improves EEMD results in that it overcomes the potential deficiencies and achieves better order spectrum representation for fault diagnosis. Based on the analysis, a diagnostic feature is designed based on the order spectra of extracted IFMs for detection and separation of gearbox faults. Experimental results show this feature is sensitive to different fault severities and robust to the influences from operating conditions and remote sensor locations

Fuzzy determination of informative frequency band for bearing fault detection

Detecting early faults in rolling element bearings is a crucial measure for the health maintenance of rotating machinery. As faulty features of bearings are usually demodulated into a high-frequency band, determining the informative frequency band (IFB) from the vibratory signal is a challenging task for weak fault detection. Existing approaches for IFB determination often divide the frequency spectrum of the signal into even partitions, one of which is regarded as the IFB by an individual selector. This work proposes a fuzzy technique to select the IFB with improvements in two aspects. On the one hand, an IFB-specific fuzzy clustering method is developed to segment the frequency spectrum into meaningful sub-bands. Considering the shortcomings of the individual selectors, on the other hand, three commonly-used selectors are combined using a fuzzy comprehensive evaluation method to guide the clustering. Among all the meaningful sub-bands, the one with the minimum comprehensive cost is determined as the IFB. The bearing faults, if any, can be detected from the demodulated envelope spectrum of the IFB. The proposed fuzzy technique was evaluated using both simulated and experimental data, and then compared with the state-of-the-art peer method. The results indicate that the proposed fuzzy technique is capable of generating a better IFB, and is suitable for detecting bearing faults

A robust fault detection method of rolling bearings using modulation signal bispectrum analysis

Envelope analysis is a widely used method for bearing fault detection. To obtain high detection accuracy, it is critical to select an optimal narrowband for envelope demodulation. Fast Kurtogram is an effective method for optimal narrowband selection. However, fast Kurtogram is not sufficiently robust because it is very sensitive to random noise and large aperiodic impulses which normally exist in practical application. To achieve the purpose of denoising and frequency band optimization, this paper proposes a new fault detector based on modulation signal bispectrum analysis (MSB) for bearing fault detection. As MSB results highlight the modulation effects by suppressing stationary random noise and discrete aperiodic impulses, the detector developed using high magnitudes of MSB can provide optimal frequency bands for fault detection straightforward. Performance evaluation results using both simulated data and experimental data show that the proposed method produces more effective and robust detection results for different types of bearing faults, compared with optimal envelope analysis using fast Kurtogram