on 03 July 20
Stream analytics has changed the way the business processes are run these days. It has many offshoots like real time sentiment analysis, Predictive maintenance analytics and so on. While real time sentiment analysis helps gauge the mood of the customer predictive maintenance helps to maintain the plant and the machinery where production takes places place.
The predictive aspect of the streaming analytics has revolutionized the way the machines are maintained. In early days either the whole process had to shut for the periodic maintenance or was forced to shut after the failure of a component. Not anymore. The Predictive maintenance analytics has changed it radically and now a component failure can be predicted well in advance and corrective measures can be employed without shutting the whole system. This has also made the need for periodic maintenance redundant.
Predictive maintenance software solutions access multiple data sources in real time to predict asset failure or quality issues so your organization can avoid costly downtime and reduce maintenance costs. Driven by predictive analytics, these solutions detect even minor anomalies and failure patterns to determine the assets and operational processes that are at the greatest risk of problems or failure. This early identification of potential concerns helps you deploy limited resources more cost effectively, maximize equipment uptime and enhance quality and supply chain processes, ultimately improving customer satisfaction.
The sensor data is used to understand equipment degradation process or predict failure based on past failure history or pattern or circumstances in which failure took place. Different data mining methods are used to detect varieties of failure modes.
An important tool is 'trend analysis' which is used to review the data to find if the asset being monitored is on an obvious and immediate downward slide toward failure. Similarly, Pattern recognition is used to decode the causal relations between the certain type of events and machine failures. The Critical range and limits is employed to verify if the data is within a critical range limit. However, machine learning schemes can be adopted to eliminate user intuition for setting these limits.
Statistical process analysis is also an important tool for predictive maintenance analysis as existing failure record data (retrieved from warranty claims, data archives, and case-study histories) is driven through analytical procedures to find an accurate model for the failure curves and the new data is compared against those models to identify any potential failures.
To summarize, Predictive maintenance analytics can be helpful in following scenarios:
1. Predict where, when, and why asset failures are likely to occur.
2. Quickly identify primary variables as part of the root-cause analysis process.
3. By better understanding asset performance and product quality, organizations will also be able to:
4. Minimize product quality and reliability issues to meet customer delivery schedules.
5. Optimize spare-parts inventory to reduce inventory costs associated with stockouts and overstocks.
6. Predict warranty claims to increase customer satisfaction.
7. Enhance sales and operations planning to reduce operations costs8. Inform upcoming issues to planning and budgeting teams prior to costly event failures occurring.
This blog is listed under Data & Information Management Community
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