An Automated method for the analysis of bearing vibration based on spectrogram pattern matching

Abstract

As a mean for non-intrusive inspection of bearing systems, the scope of predicting their condition from the acoustic vibrations liberated during their operation, utilizing signal processing methods, has been of extensive research, over decades. Vibration being highly non-stationary, time domain as well as spectral features cannot characterize its behavior. Even though spectrogram is a time-frequency domain feature extraction technique, its interpretation is tedious and perhaps, subjective. In the proposed method, the spectrogram images of the normal vibration data is compared with that of the contextual vibration, using Structural Similarity Index Metric (SSIM). It is hypothesized that the pattern similarity between the contextual spectrogram and the baseline is low when the bearing is faulty. The SSIM between the spectrogram image of normal bearing vibration data and the baseline is different from those between the baseline and vibration data corresponding to Inner Race Failure (IRF), Roller Element Defect (RED) and Outer Race Failure (ORF). Via the proposed method of spectrogram pattern matching based on SSIM, the subjectivity in the comparative interpretation of spectrogram is eliminated fully. The SSIM corresponding to the vibrations acquired from the normal and faulty bearings differ with a P value of 4.43693x 10-16. The technique can distinguish defective bearings with, 95.74% sensitivity, 96% accuracy and 100% specificity, without dismantling or open intervention

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

“…Non-ML solutions [267], [392]- [398] employ signal processing techniques for feature extraction and use similaritybased classification, providing a computationally efficient alternative to DL-based solutions. In these methods, features are extracted from available labeled normal samples, and a "normality" reference model is constructed from these features.…”

Note on cost minimization for a multi-product fabrication- distribution problem with commonality, postponement and quality assurance

“…In the manufacturing field, the most economical production lot was originally presented by Taft (1918) with simple assumption of producing single product on single machine under the condition of perfection in production process. In real life situations, however, production of nonconforming/imperfect products is inevitable owing to diverse uncontrollable factors (Porteus, 1986;Boone, Ganeshan, Guo, & Ord, 2000;Kumar, Goyal, & Singhal, 2017;Muralidharan, Vallavaraj, Mahanti, & Patidar 2017;MohanDas, Ayyanar, Susaiyappan, Kalimuthu, 2017;Villarreal et al, 2018;Arun, Lincon, & Prabhakaran, 2019). Some nonconforming items can be reworked to restore their quality, and of course with extra cost (Inderfurth, Janiak, Kovalyov, & Werner, 2006;Makarova, Shubenkova, Mavrin, & Boyko, 2017;Zhang, Wu, & Tang, 2017;Chiu, Wu, Chiu, & Hwang, 2018).…”

Optimal hybrid inventory replenishment runtime for a vendor- buyer coordinated system with breakdowns and rework/disposal of nonconforming items

“…Various numerical experiments were performed to explore the influences of changes in defective instances on the required computer times for obtaining the optimal batch-size solutions. Additional works (Al-Bahkali & Abbas, 2018;Arun, Lincon, & Prabhakaran, 2019;Ghalme, Mankar, & Bhalerao, 2017;Richter, 1996;Saari & Odelius, 2018;Sarker, Jamal, & Mondal, 2008;Shakoor, Abu Jadayil, Jaber, & Jaber, 2017;Souha, Soufien, & Mtibaa, 2018;Vujosevic, Makajic-Nikolic, & Pavlovic, 2017;Zahraee, Rohani, & Wong, 2018) studied the impact of various characteristics of unreliable facility and rework/disposal of defective products on fabrication systems and operations management.…”