Predictive maintenance is the analysis of the condition and performance of critical plant machines to reduce instances of machine failure. In the past, highly experienced plant operators may have predicted outcomes through experience, sound of operation, or other asset behaviors. Today, this is done with a variety of software tools and analysis types. Understanding predictive maintenance is important – choosing it as a solution for your plant can lead to savings by way of more operational uptime, faster diagnosis of issues, extending machine life, and estimating time to failure.
To pursue predictive maintenance as a solution for your plant, you should understand the step-by-step process:
- Data acquisition: The data collected is usually time series process data such as historian data. Common tags analyzed include current, temperature, pressure,flow, vibration, etc.
- Preprocessing the data: This includes removing outliers, filtering out meaningless data, and correcting offset time parameters.
- Identifying conditional indicators: This involves distinguishing between normal asset operation and various fault types. Examples include healthy motor operational parameters, seal leakages, worn bearings, blocked inlets, or a combination of faults. Methods to identify fault features include time-based analysis and frequency analysis.
- Training the model: After healthy operation and fault states are identified, the model is trained. This is important for understanding the accuracy of the fault indicators.
- Deploying and integrating the solution: This can be done on premise, at the edge, or in the cloud.
- Retraining the model: Retraining is based on live process data, should new features or faults appear over time. As new faults occur, our analysts can identify the issue and seek resolution with the client and client teams.
These steps encompass the value drivers of predictive maintenance, including more operational uptime, faster diagnosis of issues through fault type identification, extending machine life, and estimating time to failure. Investing in predictive maintenance can pay off for many types of industrial plants with a variety of machine types.
Contact us any time for more information on how predictive maintenance can benefit your plant and prevent downtime.
Dan Riley, Analytics Manager