Fault diagnosis of rolling element bearing using autonomous harmonic product spectrum method

Abstract

Maintenance planning plays a critical role in the process industry, where any unplanned maintenance may lead to a significant loss. Condition monitoring happens to aid maintenance planning and has become an inherent part of the maintenance activity. Physical parameters such as vibration, acoustic emission, current, etc., are used for condition monitoring, out of which vibration is the most preferred parameter and is widely used in the industry. Vibration data is measured near to bearings, which themselves are monitored for their condition, and hence rolling element bearing (REB) is the focus of this study. REBs are monitored for the presence of a fault in them as well as for their severity. Fault diagnosis of REB using harmonic product spectrum (HPS) is proposed in this study. The proposed methodology's novelty lies in the signal pre-processing step, whose output is fed to the HPS method, which is used for defective raceway identification. The efficacy of HPS is assessed with parameter optimized Variational mode decomposition (VMD) and classical bandpass filtering method as pre-processors. It is observed that the HPS delivers better diagnostic results with the VMD method than the bandpass filtering method. Non-dominated sorting particle swarm optimization algorithm is deployed for parameter optimization of VMD. HPS combined with VMD as pre-processor forms an autonomous HPS(AHPS) algorithm, whose input is measured signal and output is defect frequency. The process is so designed that a raw signal, when fed to the algorithm, delivers the result as identification of a defective raceway. Unlike previously developed methods, the proposed method needs no manual intervention. Results obtained from simulated signals and signals recorded through experiments validate that the proposed methodology can be used effectively for fault diagnosis of REB.

An autonomous method for diagnosing raceway defects and misalignment in a self-aligning rolling-element bearing

“…Where f is the original signal, δ is the Dirac distribution, k defines the number of modes, u k is signal corresponding to k th mode and w k is the central frequency of a certain mode. Using the method of Lagrange multipliers to solve the optimization problem, the augmented Lagrangian is expressed as (26) Where α is the penalty term. The parameters α and k significantly influence the VMD method f or a given input.…”

“…Liu et al 24 used his already developed piecewise function for analyzing the behavior of a deep groove ball bearing with localized while the rolling-elements followed offset, and bias trajectories. Harmonic Product Spectrum (HPS) [25][26][27] was used as a tool for the detecting the fundamental frequency from a signal having interferences from many sources. Variational mode decomposition (VMD) along with some other signal processing techniques has been used by many researchers for obtaining the signal of interest i.e.…”

Transformer winding mechanical fault diagnosis method based on closing transient acoustic vibration signal

“…Transformer winding mechanical state failure means that under the action of mechanical force or electrodynamic force, the size or position of the winding has irreversible changes, specifically for the winding overall or local size change, body displacement, winding loosening and insulation pad off, etc. [1]. The causes of mechanical failure of transformer windings can be summarized into four categories.…”

Performance evaluation of three signal decomposition methods for bearing fault detection and classification

“…This method effectively handles the EMD and its derivative above-stated limitations. Some of the literature covering the wider application of this signal decomposition technique includes bearing fault diagnosis, 25–27 biomedical image denoising, 28 and other mechanical application. 29,30 One of the most challenging aspects of employing these decomposition methods is deciding which modes to include in feature extraction to be utilized in the classification algorithms.…”