In industrial settings, compressors are used to provide air to power air tools, paint sprayers, and abrasive blast equipment, to phase shift refrigerants for air conditioning and refrigeration and to propel gas through pipelines. In our application, the compressor is used in a laser cutting machine to eliminate all the debris and cool the material at the point of contact between the workpiece and the laser beam. Failure of doing this may lead to ruining the workpiece, as the material will warp near the laser beam and also, pose a structural risk to the whole machinery as the debris might accumulate and ignite from the heat.
To address this, we will be developing a predictive maintenance solution that gathers vibration data from an oil-less compressor and uses machine learning algorithms to detect if the piston is unbalanced or if the compressor manifests an anomalous behavior.
By employing IoT devices powered by machine learning algorithms running on the edge, Predictive maintenance is closer to becoming a common practice in industrial environments, making it cheaper, more accessible and more powerful than ever. While simple in their principle of operation, predictive maintenance systems improve the Overall Equipment Effectiveness and positively impact the equipment Remaining Useful Life (RUL).
Built for Edge Impulse by the Zalmotek team