This dataset utilizes a machine vibration analysis trainer (MVAT-6 from V-TEK Associates) commonly used to train factory maintenance and operations technicians how to manually diagnose machine fault states and running performance.
Instead, we can use this equipment to train the STMicro SensorTile IoT development kit to automatically recognize the same machine states autonomously using a SensiML generated endpoint AI sensing algorithm running continuously on the ST SensorTile device. For input signals, we use the on-board IMU sensor ST LSM6DSM 6-axis iNEMO inertial module sampled at 1 kHz. The accompanying photo shows the machine configuration.
The ST SensorTile device is affixed directly to the rightmost bearing block of the MVAT-6. Results of the model can be visualized in real-time using an Android smartphone running the SensiML TestApp. All classification is accomplished on the SensorTile’s ARM M4F processor and only classified state events are sent to the smartphone (using a Bluetooth Low Energy wireless link from the on-board BlueNRG radio in the SensorTile).
The MVAT-6 machine itself consists of a DC motor and drive electronics connected to a load shaft via a replaceable flexible coupler, with the drive shaft supported by two bearing blocks. The right end of the shaft can accommodate a variety of removable aluminum disc rotors to simulate different inertial loads. One such rotor is machined to be well balanced and concentric within tight tolerance. Another such rotor is bored with a slightly offset center hole to induce an imbalanced load vibration. Yet another has a misaligned center bore hole to produce a wobbly or “cocked rotor” rotational load.
Collected Machine States
The following table lists the combinations of data collected in different machine configurations. Several files exists across each configuration within the dataset.
|Machine State>||Off||Running, No Rotor||Running, Balanced Rotor||Running, Eccentric Rotor||Running, Cocked Rotor|
For each machine configuration, raw sensor data was collected and labeled for classification states per the table above.
Additionally, other relevant metadata was also collected which could impact the sensor data classification. These include:
- Which SensorTile unit was used for processing (not ordinarily an issue but collected in case problems arose)
- The motor speed in RPM (as shown on the LED tach readout)
- The operator who performed the collection (always a good idea to annotate in case differences in technique or protocol between operators is subsequently discovered)
The chart below show a representative view of the signal waveforms for one of the collection files as viewed within SensiML Data Capture Lab: