Anyone with experience in production environment knows how important it is to minimise unplanned downtime of critical equipment. Preventative maintenance is helpful, but insufficient to prevent nasty surprises.
Today, most equipment comes with a vast array of built-in sensors. Collecting and processing this data, and using it as an input into a Machine Learning is a practical solution for critical eequipment condition monitoring and predictive maintenance.
Predictive maintenance is the process of identifying a leading indicator "signal" which reliable indicates impending component failure. Mathematically, this is not a new area of study. The advent of low-cost data collection and processing, and performant models, makes reliable real-time prediction possible. Reducing unplanned downtime to near 0 is realistic and commercially viable.
Four bearings were installed on a shaft. The rotation speed was kept constant at 2000 RPM by an AC motor coupled to the shaft via rub belts. A radial load of 6000 lbs is applied onto the shaft and bearing by a spring mechanism. All bearings are force lubricated. Rexnord ZA-2115 double row bearings were installed on the shaft as shown in Figure 1. PCB 353B33 High Sensitivity Quartz ICP accelerometers were installed on the bearing housing (two accelerometers for each bearing [x- and y-axes] for data set 1, one accelerometer for each bearing for data sets 2 and 3). Sensor placement is also shown in Figure 1. All failures occurred after exceeding designed life time of the bearing which is more than 100 million revolutions.
Three (3) data sets are included in the data packet (IMS-Rexnord Bearing Data.zip). Each data set describes a test-to-failure experiment. Each data set consists of individual files that are 1-second vibration signal snapshots recorded at specific intervals. Each file consists of 20,480 points with the sampling rate set at 20 kHz. The file name indicates when the data was collected. Each record (row) in the data file is a data point. Data collection was facilitated by NI DAQ Card 6062E. Larger intervals of time stamps (showed in file names) indicate resumption of the experiment in the next working day.
Recording Duration: October 22, 2003 12:06:24 to November 25, 2003 23:39:56 No. of Files: 2,156 No. of Channels: 8 Channel Arrangement: Bearing 1 – Ch 1&2; Bearing 2 – Ch 3&4; Bearing 3 – Ch 5&6; Bearing 4 – Ch 7&8. File Recording Interval: Every 10 minutes (except the first 43 files were taken every 5 minutes) File Format: ASCII Description: At the end of the test-to-failure experiment, inner race defect occurred in bearing 3 and roller element defect in bearing 4.
Recording Duration: February 12, 2004 10:32:39 to February 19, 2004 06:22:39 No. of Files: 984 No. of Channels: 4 Channel Arrangement: Bearing 1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing 4 – Ch 4. File Recording Interval: Every 10 minutes File Format: ASCII Description: At the end of the test-to-failure experiment, outer race failure occurred in bearing 1.
Recording Duration: March 4, 2004 09:27:46 to April 4, 2004 19:01:57 No. of Files: 4,448 No. of Channels: 4 Channel Arrangement: Bearing1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing4 – Ch4; File Recording Interval: Every 10 minutes File Format: ASCII Description: At the end of the test-to-failure experiment, outer race failure occurred in bearing 3.
This dataset is available on Kaggle and the Offical NASA Website. The dataset is in text format and has been rared, then zipped and also contain breif documentation (README) by the authors. The data set was provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati.