Predictive maintenance uses data to monitor the condition of equipment. Using fourth industrial revolution techniques means reduced failures, less machine downtime, reduced maintenance costs, and so much more. Most industrial plants collect large amounts of sensor data from their equipment. The sheer volume of data may leave most people thinking “What now?”. Using this data to inform smart business decisions requires a rare combination of both domain and statistical expertise.
Using MATLAB’s Diagnostic Feature Designer App you can perform feature selection quickly and easily. With the app, you can explore and discover feature extraction techniques without writing MATLAB code. Extract, visualise and rank features from sensor data by order of significance using statistical and dynamic modelling methods. Use your understanding of your plant-process-data to quickly draw insights and build up predictive models.
Watch this 5min video to see how quickly you can start working with operational data, to build up predictive maintenance models.
What Can I Do Next?
- Watch the video.
- Request a trial.
- See this webinar on Predictive Maintenance with MATLAB to learn how to use machine learning techniques in MATLAB to estimate remaining useful life of equipment.