Operational efficiency and minimal downtime are critical in manufacturing. Condition-based maintenance schedules maintenance based on the actual condition of equipment rather than on a predetermined schedule. Think of scheduling your car for repair based on the condition of the vehicle rather than relying on a pre-set schedule. The advent of advanced production monitoring and real-time data analytics is propelling predetermined maintenance into a more sophisticated and proactive realm known as predictive maintenance. This blog explores how production monitoring is revolutionizing maintenance strategies, elevating predetermined maintenance into condition-based and predictive maintenance, and the substantial benefits it brings to manufacturing operations.
What is Preventive Maintenance?
Traditional predetermined maintenance strategies typically rely on time-based or usage-based schedules. Machines are serviced at regular intervals based on manufacturer recommendations instead of their actual condition. While this method of predetermined maintenance reduces the likelihood of unexpected breakdowns, it often results in over-maintenance or missed opportunities for intervention.
Drawbacks of Traditional Preventive Maintenance:
- Over-Maintenance: Frequent servicing can lead to unnecessary downtime and increased maintenance costs without significantly improving reliability.
- Under-Maintenance: Rigid schedules may not account for variations in machine usage or environmental conditions, leading to unexpected failures.
- Reactive Tendencies: Unplanned downtime can still occur despite preventive efforts.
The Evolution to Condition-Based and Predictive Maintenance
Condition-Based Maintenance focuses on monitoring the actual condition of the machine to determine when maintenance should be performed. It relies on various indicators such as vibration, temperature, and pressure to assess equipment health. The goal of condition-based maintenance is to prevent failures and extend the life of machinery by performing maintenance only when needed.
Predictive Maintenance takes a step further by not only monitoring the current state of machinery but also using advanced manufacturing analytics to predict future failures. Predictive maintenance leverages historical and real-time data, coupled with machine learning algorithms, to forecast potential issues before they occur, allowing for even more proactive and efficient maintenance.
Both stages of maintenance rely on real-time data that can be understood easily by the manufacturing team. A dashboard filled with incomprehensible charts and figures serves no one.
Implementing Predictive Maintenance with Production Monitoring
To effectively transition from predetermined maintenance to condition-based or predictive maintenance, manufacturers need a way to collect real-time data and a robust production monitoring system to interpret that data. Here are key steps for successful implementation:
- Deploy Advanced Sensors and IoT Devices:
- Install sensors on critical machinery to monitor key performance indicators. Ensure comprehensive coverage to capture all relevant data points.
- Use IoT devices to facilitate seamless data transmission to central monitoring systems.
- Develop Data Collection and Integration Framework:
- Establish a robust data collection infrastructure capable of handling large volumes of real-time data. Integrate data from various sources to provide a complete picture of equipment health.
- Use Cloud computing to process data locally, reducing latency and ensuring immediate anomaly detection.
- Leverage Advanced Analytics and Machine Learning:
- Implement predictive analytics tools and machine learning algorithms to analyze historical and real-time data. Continuously refine predictive models to improve accuracy.
- Utilize AI-driven insights to identify patterns and predict potential failures.
- Automate Maintenance Workflows:
- Define maintenance thresholds and triggers within the production monitoring system. Ensure that alerts are automatically generated when conditions exceed acceptable ranges.
- Integrate maintenance management systems to automate the generation of work orders and streamline maintenance processes.
- Train and Educate Personnel:
- Provide training for maintenance teams on the use of production monitoring systems and predictive analytics tools. Emphasize the importance of proactive maintenance strategies.
- Foster a culture of continuous improvement, encouraging teams to utilize data-driven insights to optimize maintenance practices.
How is Predictive Analytics Used in Manufacturing?
Production monitoring systems play a crucial role in the transition from predetermined maintenance to condition-based and predictive maintenance. These automated systems eliminate the middle-man of manual reporting to collect real-time data from the factory floor, providing a comprehensive view of equipment performance and health. Here are a few ways that production monitoring facilitates this transition:
Comprehensive Data Collection
- Sensors and IoT Devices: Modern smart factory systems are equipped with an array of sensors and IoT devices that continuously gather data on key parameters such as vibration, temperature, humidity, and pressure. Data is transmitted in real-time to a centralized dashboard.
- Data Integration: The integration of various data sources ensures a holistic view of machinery conditions, enabling more accurate assessments and predictions.
Real-Time Analytics
- Immediate Anomaly Detection: Real-time manufacturing analytics allow for the instant identification of anomalies. For instance, a sudden spike in vibration levels can indicate an impending bearing failure, triggering an alert for immediate inspection.
- Trend Analysis: Continuous monitoring enables the analysis of data trends over time. Deviation from normal operating patterns can signal potential issues, providing an early warning system for maintenance teams.
Predictive Modeling
- Historical Data Utilization: By analyzing historical data alongside real-time data, predictive models can be developed to forecast future equipment behavior. Machine learning algorithms enhance these models by learning from each new data point, improving prediction accuracy over time.
- Failure Prediction: Predictive models can estimate the remaining useful life of components, allowing maintenance to be scheduled just before a predicted failure, thus avoiding unnecessary downtime.
Automated Maintenance Triggers
- Threshold-Based Alerts: Production monitoring systems can be programmed to trigger alerts based on predefined thresholds. When a parameter exceeds its normal range, the system generates an alert, prompting immediate action.
- Automated Work Orders: Advanced systems can automatically generate maintenance work orders when predictive models indicate an imminent failure. This automation streamlines the maintenance process, ensuring timely interventions. H&T Waterbury integrated Mingo Smart Factory with their CMMS software Fiix to automatically generate maintenance tickets.
Case Study
“We had to know the availability of a spare part as opposed to a refurbished one. That allowed us to quickly do an ROI on new equipment or spare parts. To say – we need this, it’s costing us X amount of money, and it’ll pay for itself in a week. Let’s go ahead and do it.”
Chris Mericas, Louisiana Fish Fry
Benefits of Predictive Maintenance Enabled by Production Monitoring
1. Increased Equipment Reliability: Predictive maintenance significantly enhances equipment reliability by addressing potential issues before they lead to failures. Continuous monitoring and advanced analytics ensure that machinery operates within optimal parameters, reducing the likelihood of unexpected breakdowns.
2. Reduced Downtime: By predicting and preventing failures, predictive maintenance minimizes unplanned downtime. Maintenance can be scheduled during non-productive hours, ensuring that production schedules remain unaffected.
3. Cost Efficiency: Predictive maintenance optimizes the use of maintenance resources. By intervening only when necessary, it reduces the frequency of routine maintenance tasks, leading to substantial cost savings. Additionally, preventing major failures avoids the high costs associated with emergency repairs.
4. Extended Equipment Lifespan: Timely maintenance interventions prevent excessive wear and tear, extending the lifespan of machinery. This prolongs the period between major overhauls and replacements, resulting in long-term capital savings.
5. Enhanced Safety: Real-time monitoring and predictive analytics enhance workplace safety by ensuring that machinery operates within safe parameters. Early detection of hazardous conditions allows for prompt corrective actions, reducing the risk of accidents and injuries.
How H&T Waterbury Integrated Mingo and Fiix to Move to Condition-Based Maintenance
By integrating Mingo Smart Factory and Fiix CMMS software, H&T Waterbury was able to move towards condition-based maintenance. This move improved communications between teams, and helped them diagnose and solve problems quickly. Data transmitted to Mingo to understand trends, reporting, and analysis of data. In Fiix, the maintenance team can schedule maintenance activities, track inventory, and maintain workflow.
The Mingo Smart Factory & Fiix integration:
- Enabled communication between maintenance and production
- Moved the maintenance team to condition-based maintenance
- Triggered preventive maintenance work orders
- Collected process data to detect anomalies
- Eliminated the finger-pointing
Using Production Monitoring to Enhance Maintenance Procedures
The integration of production monitoring and real-time data is transforming predetermined maintenance to condition-based and predictive maintenance, providing manufacturers with powerful tools to enhance operational efficiency and reliability. By leveraging comprehensive data collection, advanced analytics, and automated maintenance workflows, predictive maintenance offers significant benefits, including increased equipment reliability, reduced downtime, cost savings, extended equipment lifespan, and enhanced safety.
For manufacturing managers and continuous improvement specialists, embracing predictive maintenance is not just an option—it is a necessity to stay competitive in today’s fast-paced industrial landscape. Investing in a robust production monitoring system that will grow with the business will pave the way for a more efficient, cost-effective, and resilient manufacturing operation tomorrow.
One of the most important considerations to keep in mind when selecting a production monitoring system is finding one that provides data-driven insights. The dashboard and operator inputs need to be easy to use by everyone on the team.
Interested to see how Mingo Smart Factory can help your factory move from predetermined maintenance to condition-based or predictive maintenance?
