Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings

Abstract

Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.

Early fault diagnosis of rolling bearings based on parameter-adaptive multipoint optimal minimum entropy deconvolution adjusted and dynamic mode decomposition

“…Figure 1 shows the flowchart of PA-MOMEDA, where T i and T b refer to the minimum and maximum fault periods for several types of bearing fault signals, f s is the sampling frequency, and f is the minimum fault frequency. The filter length will be set according to the empirical formulas of [16,17]. In the following flowchart, K t is an iteration number in the current stage, and K s is a number when iteration terminates.…”

“…To extract the repeated pulses of bearing fault signal effectively, methods for rotating machinery fault diagnosis using fault cycle information have been proposed. Representative methods include minimum entropy deconvolution (MED) [15], maximum correlated kurtosis deconvolution (MCKD) [16], multipoint optimal MED adjusted (MOMEDA) [17], and blind deconvolution (BD) based on cyclostationarity maximization [18]. These four methods use kurtosis, correlation kurtosis, multiple D-norms (MDNs), and cyclic smoothness index as objective functions, ensuring that hidden periodic pulses in the measured signal can be enhanced effectively.…”

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

“…In practical applications, the actual fault period differs from theoretical value due to irrelevant information. In cases where the fault type and pulse period of the rotating machinery cannot be known in advance, it is meaningful to select the appropriate fault period adaptively [46]. Therefore, a fault-period estimation method based on the Teager energy operator for the ACF is proposed in this study to estimate the fault period of the vibration signals accurately.…”

A gear fault feature extraction method based on adaptive AR model and MOMEDA

“…Although this method was shown to seek the good filter directly instead of iterative operation, it is also not suitable for continuous periodic shock signal extraction. To address some shortcomings of traditional deconvolution method, a new deconvolution method called Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) was proposed, which constructs an objective vector according to the pre-estimated fault frequency [8][9][10]. The D-norm maximum criterion is utilized to find the optimal filter in MOMEDA.…”