Showing papers by "Yaguo Lei published in 2013"

Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds.

Abstract: Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of rolling element bearings. Conventional diagnostic methods are based on the stationary assumption, thus they are not applicable to the diagnosis of bearings working under varying speed. This constraint limits the bearing diagnosis to the industrial application significantly. In order to extend the conventional diagnostic methods to speed variation cases, a tacholess envelope order analysis technique is proposed in this paper. In the proposed technique, a tacholess order tracking (TLOT) method is first introduced to extract the tachometer information from the vibration signal itself. On this basis, an envelope order spectrum (EOS) is utilized to recover the bearing characteristic frequencies in the order domain. By combining the advantages of TLOT and EOS, the proposed technique is capable of detecting bearing faults under varying speeds, even without the use of a tachometer. The effectiveness of the proposed method is demonstrated by both simulated signals and real vibration signals collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Analyzed results show that the proposed method could identify different bearing faults effectively and accurately under speed varying conditions.

Fault diagnosis of rotating machinery based on an adaptive ensemble empirical mode decomposition.

Abstract: The vibration based signal processing technique is one of the principal tools for diagnosing faults of rotating machinery. Empirical mode decomposition (EMD), as a time-frequency analysis technique, has been widely used to process vibration signals of rotating machinery. But it has the shortcoming of mode mixing in decomposing signals. To overcome this shortcoming, ensemble empirical mode decomposition (EEMD) was proposed accordingly. EEMD is able to reduce the mode mixing to some extent. The performance of EEMD, however, depends on the parameters adopted in the EEMD algorithms. In most of the studies on EEMD, the parameters were selected artificially and subjectively. To solve the problem, a new adaptive ensemble empirical mode decomposition method is proposed in this paper. In the method, the sifting number is adaptively selected, and the amplitude of the added noise changes with the signal frequency components during the decomposition process. The simulation, the experimental and the application results demonstrate that the adaptive EEMD provides the improved results compared with the original EEMD in diagnosing rotating machinery.

A KPI-related multiplicative fault diagnosis scheme for industrial processes

Abstract: In this paper, a key performance indicator (KPI) related multiplicative fault diagnosis scheme is proposed for static industrial processes. This scheme is developed for an alternative algorithm to the standard partial least squares (PLS) based process monitoring, where no design parameter like “latent variable number” is involved. Based on both normal and faulty data sets, the multiplicative fault information is firstly estimated. With this knowledge, the most critical low-level control loop/component is further identified. Different from the existing data-driven additive fault diagnosis approaches, this scheme aims to handle the second order statistics, which is of fatal importance for KPI-related fault diagnosis. Finally, an academic example is investigated to illustrate the functionality of this scheme.

Flexible time domain averaging technique

Abstract: Time domain averaging(TDA) is essentially a comb filter, it cannot extract the specified harmonics which may be caused by some faults, such as gear eccentric Meanwhile, TDA always suffers from period cutting error(PCE) to different extent Several improved TDA methods have been proposed, however they cannot completely eliminate the waveform reconstruction error caused by PCE In order to overcome the shortcomings of conventional methods, a flexible time domain averaging(FTDA) technique is established, which adapts to the analyzed signal through adjusting each harmonic of the comb filter In this technique, the explicit form of FTDA is first constructed by frequency domain sampling Subsequently, chirp Z-transform(CZT) is employed in the algorithm of FTDA, which can improve the calculating efficiency significantly Since the signal is reconstructed in the continuous time domain, there is no PCE in the FTDA To validate the effectiveness of FTDA in the signal de-noising, interpolation and harmonic reconstruction, a simulated multi-components periodic signal that corrupted by noise is processed by FTDA The simulation results show that the FTDA is capable of recovering the periodic components from the background noise effectively Moreover, it can improve the signal-to-noise ratio by 79 dB compared with conventional ones Experiments are also carried out on gearbox test rigs with chipped tooth and eccentricity gear, respectively It is shown that the FTDA can identify the direction and severity of the eccentricity gear, and further enhances the amplitudes of impulses by 35% The proposed technique not only solves the problem of PCE, but also provides a useful tool for the fault symptom extraction of rotating machinery