The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data. (1st April 2019) - Daga, Alessandro Paolo; Fasana, Alessandro; Marchesiello, Stefano; Garibaldi, Luigi
Highlights: Benchmark data for bearings: 2 triaxial accelerometers, 7 health conditions varying severity and location. Simple time series features from the accelerometric measurements. Feature space investigated with ANOVA and LDA classificator. Damage detection via Outlier Analysis based on Mahalanobis distance. Quick, simple and fully independent from human interaction - optimal in the stationary case. Abstract: Nowadays, machines-diagnostics via vibration monitoring is rising an always growing interest thanks to the huge and accurate amount of health information which could be extracted by the raw data coming from accelerometers. Damage severity, type and location of a fault are the kind of information which are buried in the time records. The scope of this paper is double: first, to present the huge amount of data which have been acquired on the rolling bearing test rig of the Dynamic and Identification Research Group (DIRG), in the Department of Mechanical and Aerospace Engineering at Politecnico di Torino and to share them with the scientific community; secondly, to present a statistical approach analysis and its performances as example of a simple technique to be fruitfully adopted for comparison. To this goal, a detailed presentation of the test rig is given, which comprehends different working conditions up to 30, 000 rpm, damage types and levels, various sensors positions and directions as well as an endurance test. The related time records can be downloaded fromftp://ftp.polito.it/people/DIRG_BearingData/ . Afterword, tried-and-tested statistical tools are exploited to learn the information about bearing damages from this massive amounts of data. This "data mining" will be performed using inferential statistical techniques as analysis of variance (ANOVA), applied on usual statistical features, which characterize of the signal. A linear discriminant analysis (LDA) in the configuration proposed by Fisher will be also used to see if the data were classifiable in a multidimensional space with this basic algorithm. Finally, an Outlier Analysis based on Mahalanobis distance will be formulated, so as to distinguish a damage condition from the healthy state (training data), compensating when possible for environmental (temperature) and operational (speed and load) variations.