As we approach the year’s end, winter shutdowns are a reality for many manufacturing plants. This is not only a time for workers to recharge and spend time with the ones that mean the most to them, it’s also an opportunity to ensure equipment is in proper working order.
Here's a comprehensive checklist for predictive maintenance for industrial process equipment:
Maintenance strategy development
- Critical Equipment Identification: The first step in your predictive maintenance plan involves identifying and prioritizing critical equipment in mechanical systems. These are the workhorses of your operation and the money-makers of your business. Prioritize equipment based on criticality to process and downtime costs (e.g., compressors, pumps, motors).
- Define Thresholds: From there, define acceptable thresholds in machinery like vibration, temperature, pressure and other key performance indicators (KPIs) for each asset.
- Scheduled Maintenance Optimization: Optimize your scheduled maintenance based on predictive data to adjust traditional preventive maintenance schedules and move from time-based to condition-based maintenance.
Equipment inspections and performance evaluation
- Periodic Physical Inspections: As much as machines help with everyday tasks, nothing compares to a good old-fashioned physical inspection. By performing periodic physical inspections, you can uncover any potential issues that a regular sensor might miss.
- Lubrication & Wear Checks: As robust as industrial machinery components are, they are prone to everyday wear and tear. By conducting regular checks on lubrication quality, bearing wear and seal integrity, these inspections could reveal issues before they become big problems in the future.
- Alignment and Calibration: Check alignment of rotating equipment, such as motors, pumps, and compressors. Ensure that calibration of instruments and sensors is maintained.
Failure mode and root cause analysis
If an issue happens, this is where the diagnosis takes place.
- Failure Data Collection: Gathering detailed data—such as audio recordings, thermal images and vibration data from the mechanical system—will help to discover and analyze the root cause.
- Historical Analysis: A look back at historical data may also reveal trends and help forecast any equipment failures down the road.
- Feedback Loop: Incorporate learnings from past failures to improve the predictive models and fine-tune threshold settings.
Asset lifecycle management
- Asset Health Monitoring: Just as people age, so do industrial machinery and mechanical systems. As these components get older, it’s important to monitor the health of these critical assets and use that data to adjust predictive maintenance plans.
- Replacement Planning: When it comes to replacing equipment, it’s always better to be a week too early rather than a week too late. Predictive data will indicate the typical lifespan of these machines. Use your data to predict when equipment is nearing the end of its useful life and plan for replacement or major overhauls before catastrophic failures occur.
- Spare Parts Inventory: The same goes for the lifespan of parts as well, as the time will eventually come for components to be replaced. Predictive data will forecast and optimize the spare part inventory to keep the proper amount of backups on hand, and predict the timing of replacements
Reporting and documentation
- Real-Time Alerts: Keeping records and documentation of all reports is very important, starting with real-time alerts to warn you of any abnormal conditions based on predictive data thresholds, e.g. a sudden increase in vibration or temperature along the assembly line.
- Maintenance Reporting: It is also critical to keep maintenance reports up to date, and update regularly. Ensure reports include predictive maintenance data, actions taken, and outcomes, which can be reviewed regularly.
- Documentation of Interventions: by documenting any predictive maintenance activities, including inspections, sensor data analysis, and corrective actions; this helps create an audit trail to refer to in the case of any issues later on.
Collaboration and communication
It takes an entire team of dedicated people to make this operation run smoothly, and that includes a robust partnership between maintenance teams, operations and data scientists.
- Cross-functional collaboration: Encourage collaboration between different teams to refine predictive models and improve equipment reliability.
- Feedback from Operators: Operators often notice subtle changes in equipment behaviour that sensors may not immediately detect. Ensure a process for operators to communicate anomalies to the maintenance team.
Roberts Onsite—your predictive maintenance solution
Once employees return from the winter shutdown, it’s important to have everything ready so your factory doesn’t miss a beat. By putting a predictive maintenance plan in place ahead of time, companies can save money by avoiding downtime, improving safety and optimizing maintenance resources.
Our predictive maintenance team can help implement a preventative maintenance winter shutdown plan, allowing your team to hit the ground running when it's time to return to work.