“Predictive maintenance is the analysis of the condition and performance of critical equipment in a plant to reduce the cases of failure of these components. currently, this can be done with a variety of software tools and analysis types.. In the article below, we explain a little more about this type of maintenance..“
In the past, Highly experienced production plant operators may have predicted failures through experience., operating sound or other equipment behaviors. currently, this is done with a variety of software tools and analysis types..
Understanding predictive maintenance is important and choosing it as a solution for your plant can lead to savings through more uptime, faster problem diagnosis, extending machine life and estimating failure time.
To employ the predictive maintenance concept as a solution for your plant, you must understand the step-by-step process.:
- Data acquisition: the data collected are generally those referring to time series processes. They are variables normally analyzed, the current, temperature, pressure, flow, vibration, etc.
- Data pre-processing: this includes removing outliers., meaningless data filtering and possible time-related parameter corrections.
- Identification of conditional indicators: involves distinguishing between normal component operation and various types of failure.. Examples include healthy engine operating parameters, sealing leaks, worn bearings, blocked entries or a combination of failures. Methods to identify failure characteristics include time-based analysis and frequency analysis..
- train the model: after full operation and failure modes are identified, the model is trained. This is important to understand the accuracy of failure indicators..
- Solution implementation and integration: involves the practical and active application of the necessary measures to mitigate the incidence of failures based on the data obtained. This can be done in a timely or systemic way..
- Model retraining: retraining is based on real-time process data, if new features or glitches appear over time. As new failures occur, analysts must identify the problem and seek a solution aiming at the continuous improvement of the process.
These steps substantiate the value of predictive maintenance, including more uptime, faster problem diagnosis by identifying the type of failure, longer machine life and more accurate time-to-failure estimation. Anyway, it is clear that investment in predictive maintenance can compensate for many types of industrial installations with various types of machines.
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