Machine Vibration Monitoring for Diagnostics through Hypothesis Testing

• Published in: Information (Basel) Vol. 10; no. 6; p. 204
• Main Authors: Daga, Alessandro Paolo, Garibaldi, Luigi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2019
• Subjects: Accelerometers; Aerospace engineering; Algorithms; bearings diagnostics; classification; condition-based monitoring; Damage detection; Datasets; Discriminant analysis; False alarms; Feature extraction; Hypotheses; hypothesis testing; linear discriminant analysis; Machinery condition monitoring; Mahalanobis distance; Multivariate analysis; Noise; Nondestructive testing; novelty detection; Pattern recognition; Preventive maintenance; principal component analysis; Principal components analysis; Signal processing; Statistical methods; Vibration monitoring
• DOI: 10.3390/info10060204
• ISSN: 2078-2489

Nowadays, the subject of machine diagnostics is gathering growing interest in the research field as switching from a programmed to a preventive maintenance regime based on the real health conditions (i.e., condition-based maintenance) can lead to great advantages both in terms of safety and costs. Nondestructive tests monitoring the state of health are fundamental for this purpose. An effective form of condition monitoring is that based on vibration (vibration monitoring), which exploits inexpensive accelerometers to perform machine diagnostics. In this work, statistics and hypothesis testing will be used to build a solid foundation for damage detection by recognition of patterns in a multivariate dataset which collects simple time features extracted from accelerometric measurements. In this regard, data from high-speed aeronautical bearings were analyzed. These were acquired on a test rig built by the Dynamic and Identification Research Group (DIRG) of the Department of Mechanical and Aerospace Engineering at Politecnico di Torino. The proposed strategy was to reduce the multivariate dataset to a single index which the health conditions can be determined. This dimensionality reduction was initially performed using Principal Component Analysis, which proved to be a lossy compression. Improvement was obtained via Fisher’s Linear Discriminant Analysis, which finds the direction with maximum distance between the damaged and healthy indices. This method is still ineffective in highlighting phenomena that develop in directions orthogonal to the discriminant. Finally, a lossless compression was achieved using the Mahalanobis distance-based Novelty Indices, which was also able to compensate for possible latent confounding factors. Further, considerations about the confidence, the sensitivity, the curse of dimensionality, and the minimum number of samples were also tackled for ensuring statistical significance. The results obtained here were very good not only in terms of reduced amounts of missed and false alarms, but also considering the speed of the algorithms, their simplicity, and the full independence from human interaction, which make them suitable for real time implementation and integration in condition-based maintenance (CBM) regimes.