Manufacturing businesses utilize predictive maintenance technologies to mitigate the high maintenance costs and avoid breakdowns in equipment that can potentially have adverse effects on their profitability. The use of sensors and business intelligence software to predict equipment failure and avert breakdowns is known as predictive maintenance.
Since the emergence of the Industrial Internet of Things, many manufacturing companies have found it difficult to maintain and track the voluminous data collected by their equipment. Organizations seek effective methods to leverage this data to transition from preventive maintenance methods to predictive maintenance. With a predictive maintenance strategy in place, organizations can simply carry out repair work on equipment by detecting anomalies in performance or condition, reported by the monitoring system. This approach increases the average time between breakdowns, decreases unscheduled downtime and improves uptime, essentially ensuring business continuity. Even though the approach of monitoring the condition of equipment and the application of Al and ML in maintenance are not new, crafting an end-to-end predictive maintenance strategy requires a thorough assessment and a solid grasp of the technology.
How to Implement Predictive maintenance
1. Plan ahead of time and start small.
To eliminate the possibility of getting carried away by the “technology hype” around Industry 4.0 and ending up investing in equipment that is not actually required, it is advisable to start small, evaluate and adapt, and then use the information gathered to make effective decisions.
2. Establish Critical Assets
Establishing a business’s most crucial assets is the primary and most essential task. Taking into account the advantages offered by predictive maintenance, it may seem lucrative to apply the strategy for monitoring all equipment. However, this could be counter-productive with the excess amount of data that would be generated, and the cost involved with the purchase and setup of numerous sensors.
Assets that require constant attention and are prone to breakdowns should be the first ones to be included in a predictive maintenance system. Additionally, machinery that is paramount to sustained business operations should be considered first.
3. Collect actionable data and create a database.
Data from various sources, such as machine logs or historical paper trails in the form of maintenance logs, should be collected and stored for analysis. Gathering data from CMMS, business software spanning across different divisions and feedback from maintenance professionals and technicians who regularly work with the equipment can assist in greater visibility into the condition of the equipment.
By integrating this data, businesses can establish a comprehensive understanding of their assets and uncover patterns that might have been otherwise overlooked. For instance, merging data from temperature, vibration and repair records can help identify patterns and abnormalities that can be used to precisely forecast when equipment could fail. To prevent manual errors and unnecessary delays, it is crucial to ensure that the data being gathered is of high quality and offers real-time and accurate insights into the equipment.
4. Utilize IoT Sensors and Conditional Monitoring
IoT sensors can be leveraged to identify and eliminate any gaps in the data being collected. Investing in condition monitoring sensors is necessary to put predictive maintenance into practice. Condition monitoring entails keeping a close watch on a machine’s health and efficiency on a regular basis in order to spot any discrepancies in standard operating parameters. This enables immediate action by allowing maintenance staff to keep track of assets offsite and receive immediate alerts when anomalies are detected.
5. Deploying Software for Predictive Maintenance
Business intelligence tools are used to examine equipment data. These systems streamline the process of monitoring equipment condition and organize maintenance activities by functioning as a central hub for collecting data, analysis, visualization and reporting. The BI tool will send notifications to the maintenance teams if it detects any anomalies or outliers in the functioning of machinery. For example, if the team requests to be notified concerning excessive vibrations, the system’s developers will set up an algorithm that evaluates data and forecasts whenever the vibration reaches a level that indicates that repairs may be required.
The quality of insights generated by business intelligence tools determines the effectiveness of the entire predictive maintenance process. Thus, when creating a predictive maintenance strategy, it is important to consider whether the BI tool at work can deliver real-time and accurate insights.
Conclusion
Maintenance managers can ensure uninterrupted production and prevent unscheduled downtime by regularly checking equipment condition through data generated by IoT sensors and an effective predictive maintenance platform. Businesses can actively sustain their assets by lowering expenses and preventing costly repairs. Predictive maintenance platforms are poised to develop further, gradually becoming more accurate and efficient, making them a vital tool in modern manufacturing processes.
