The manufacturing industry is powered by heavy machinery, complex factory equipment, and massive amounts of infrastructure. From traditional assembly line conveyor belts to advanced robotics; from compressor units to CNC machines, your manufacturing business depends on the availability, reliability, and efficiency of these physical assets. This guide will highlight the growing role of predictive maintenance in manufacturing and take a deeper look at the advanced technology that makes this strategy possible.
Maintenance management challenges in the manufacturing industry
Maintenance management in the manufacturing industry presents a number of unique challenges including complex machinery, high-volume production demands, strict delivery deadlines, elevated safety risks, and stringent regulatory requirements.
Moreover, because so many of these assets work in close coordination with one another during the production process, the failure of even one of these specialized assets can bring your entire operation to a grinding halt. This is why the work of maintenance and facilities management teams is so vital, and why there is a growing emphasis on predictive maintenance in manufacturing.
The role of technology in maintenance management
A proactive maintenance program is essential for managing these challenges by placing less emphasis on reactive repairs, and more on optimizing equipment performance, maximizing output, and minimizing unplanned downtime.
Facilities managers and maintenance technicians use increasingly sophisticated tools to achieve these goals. In addition to comprehensive maintenance management platforms, manufacturing firms are increasingly turning to predictive maintenance—a strategy that leverages data analytics, IoT sensors, and machine learning to anticipate equipment failures before they occur.
Maintenance strategies such as predictive maintenance are increasingly popular among asset-intensive industries like energy production, mining, and construction. However, the technology that powers predictive maintenance is proving uniquely valuable for manufacturing firms. The combination of real-time machine performance monitoring and more precise forecasting makes it possible for your maintenance team to move with agility, to prevent minor issues from cascading into systemwide failures, and to ensure that your manufacturing operation stays on track.
What is predictive maintenance?
As noted above, predictive maintenance is a proactive maintenance strategy that uses advanced predictive data analysis and condition monitoring technologies to detect deviations from optimal or typical performance and identify issues that could grow into equipment breakdowns if left unchecked. Predictive maintenance draws on several sources of data in order to allocate maintenance resources with greater precision and efficiency.
By leveraging real-time data from sensors, IoT devices, and historical performance metrics, facilities managers and maintenance technicians can identify potential issues before they lead to equipment breakdowns. This approach helps minimize unplanned downtime, reduce maintenance costs, and extend the lifespan of machinery.
How predictive maintenance compares to other maintenance strategies
When considering a more advanced maintenance strategy, it is important to consider where your organization is at on its journey. While there are many different ways to develop a strategic maintenance program that works for your manufacturing plant and incorporates a predictive approach, it is important to understand how the different strategies differ so they can be applied where they make the most sense.
Predictive maintenance versus preventive maintenance
Preventive maintenance is a proactive maintenance strategy that relies on routine servicing, regular equipment inspections, and the completion of maintenance tasks based on timed intervals or usage triggers. With preventive maintenance, tasks are performed on schedule regardless of the equipment’s performance or condition, which can reduce the frequency and severity of equipment failure, but can also cost your maintenance team time and resources.
Predictive maintenance is designed to avoid those costs by using real-time condition monitoring and historical data to initiate maintenance tasks preemptively, but only as needed.
Predictive maintenance versus reactive maintenance
Reactive Maintenance, which is sometimes also referred to as “run to failure” maintenance, is a strategy in which maintenance activities are only triggered after equipment malfunctions or fails. This approach is inherently less efficient and cost-effective than either preventive or predictive maintenance strategies. Manufacturing operations that rely on a predominantly reactive maintenance strategy risk more severe and costly equipment failures, more frequent downtime incidents, and shorter equipment lifespan.
Predictive maintenance minimizes the need for reactive strategies by identifying issues in real time and providing the opportunity for proactive corrective action.
How do manufacturers use predictive maintenance?
Manufacturers use predictive maintenance to predict and prevent potential failures before they can occur. Predictive maintenance uses advanced condition monitoring technologies and data analytics to gain real-time visibility into machinery health and initiate maintenance actions as needed.
There are a number of ways to detect abnormal or suboptimal functionality in critical manufacturing equipment including vibration, temperature, and lubrication. Below, we take a closer look at how manufacturers use these indicators in predictive maintenance settings.
Vibration Analysis
Vibration analysis uses vibration sensors to monitor the health of rotating manufacturing equipment, such as motors, pumps, and compressors. Unusual vibration patterns may be an indication of imbalance or misalignment.
Thermal Imaging
Thermal imaging and infrared thermology use infrared cameras to detect temperature variations in machinery and electrical components. For instance, spikes in temperature on an automotive assembly line may be an indication that certain critical components are overheating, and could therefore be at an elevated risk of failure.
Oil Analysis
Oil analysis is a predictive maintenance technique that evaluates the condition of lubricants in machinery, with regular oil sampling and testing revealing the presence of contaminants, wear particles, and chemical breakdown. These findings may reveal that your equipment’s lubrication is degrading and must be replaced or upgraded before your equipment sustains permanent damage.
Acoustic Emission Monitoring
Acoustic emission monitoring tracks the sound waves produced by the equipment during operation. Changes in these sound patterns may indicate worn components, low lubrication levels, or parts that need replacement.
Predictive Analytics Software
Predictive maintenance in manufacturing depends on numerous data sources to take corrective action and anticipate the need for routine maintenance. Maintenance teams today use increasingly sophisticated analytical software to integrate data findings from IoT sensors, historical performance, maintenance logs, and more to more effectively forecast equipment health and maintenance needs.
Benefits of predictive maintenance for manufacturers
Predictive maintenance in manufacturing offers a wide array of benefits including:
- Decreased unplanned downtime through proactive maintenance as well as off-peak service scheduling and strategically planned downtime
- Improved productivity as a consequence of more optimal equipment performance, fewer unexpected breakdowns, higher output rates and improved overall productivity
- Cost savings through lower repair costs, fewer emergency repairs, and optimized inventory management for spare parts
- Extended equipment lifespan through reduced wear and tear, less severe breakdown incidents, and stricter adherence to manufacturer recommendations
- Enhanced safety through reduced incidence of malfunction-related injury, accident, or fatality
- Improved regulatory compliance as a consequence of enhanced rapid response capabilities
- Refined resource allocation, with maintenance teams prioritizing tasks, delegating technicians, and designating resources based on data-driven insights
- Data-driven decision making, with predictive maintenance delivering valuable insights into operational efficiency, equipment performance, and opportunities for process improvements
Predictive maintenance challenges
Predictive maintenance strategies offer plenty of proven benefits. But manufacturers that choose to implement a predictive maintenance approach may encounter some of these common challenges:
- High upfront costs for technology, such as sensors, data analytics software, and IoT infrastructure can be a substantial barrier to entry for smaller businesses and resource-constrained companies.
- Integrating data from machinery, sensors, and existing maintenance records into a cohesive system can be complex and time-consuming, especially if organizations use incompatible systems or legacy equipment that lacks connectivity.
- Specialized personnel and training may be required to analyze data and interpret insights effectively, which can be costly and difficult to obtain in a competitive labor market.
- Internal resistance may demand effective change management, with the transition from (or integration with) preventive or reactive maintenance requiring a cultural shift within the organization.
- Implementation may be complicated as maintenance teams navigate advanced technological ecosystems, establish processes for data collection, define the right metrics, plan maintenance schedules, and more.
- Strict regulations dictate maintenance and reporting procedures, which means that predictive maintenance techniques must align with sometimes complex compliance requirements.
Predictive maintenance technology
Without proper management, the challenges cited above may very well derail the successful implementation of your predictive maintenance program. However, the right maintenance management software can help your maintenance team better navigate these potential issues. A Computerized Maintenance Management System (CMMS) should play a central role in helping your organization implement, leverage, and continuously improve a predictive maintenance program.
A CMMS will perform several key functions in your predictive maintenance program including:
- Centralization of data from various sources, including equipment performance, maintenance history, and inventory management
- Automated maintenance scheduling based on historical data, predictive insights, and condition-based triggers
- Inventory management including real-time visibility into stock levels, automated reorder triggers, and real-time location tracking for all assets and spare parts
- Reporting and analytics tools that allow organizations to track equipment performance trends, identify recurring issues, evaluate the effectiveness of existing maintenance strategies, and work toward continuous improvement
- Streamlined work order management, allowing maintenance teams to create, assign, and track maintenance tasks efficiently
- Integration with IoT devices and sensors to collect real-time data from equipment, perform advanced predictive analytics, and provide alerts when anomalies are detected
Additional predictive maintenance tools
The integration between CMMS solutions and condition-monitoring technology is essential to the success of your predictive maintenance program. Your arsenal of tools may include some of the following solutions:
- Internet of Things (IoT) sensors, which gather real-time data on equipment performance, including temperature, vibration, and operating conditions
- Advanced analytics tools, which use machine learning algorithms to deliver insights on historical and real-time data
- Thermal imaging cameras, which are used to detect temperature variations in equipment
- Vibration sensors, which monitor the condition of rotating equipment in order to detect misalignments, imbalances, or wear in machinery
10 steps for introducing a predictive maintenance program
An effective predictive maintenance program must go hand-in-hand with a robust preventive maintenance framework. Below, we provide step-by-step instructions on how your manufacturing team can build an integrated preventive and predictive maintenance program:
- Assess current maintenance practices, including preventive and reactive practices. Identify strengths, weaknesses, and areas for improvement. This step will help you identify the goals of both your preventive maintenance and predictive maintenance programs, whether you wish to reduce repair costs, lower unplanned downtime, or extend the lifespan of your equipment.
- Establish a preventive maintenance program in which you develop a routine maintenance schedule based on manufacturer recommendations and equipment usage; standardize maintenance procedures for different types of equipment; and provide your maintenance personnel with comprehensive training in preventive maintenance practices.
- Identify critical assets that are costly to repair, have a history of failure, or whose failure might significantly impact production. This criticality assessment will help you identify the equipment that should receive top priority in maintenance efforts.
- Invest in the necessary technology including a CMMS, relevant IoT sensors, and any additional data analytics that might support your goals.
- Gather historical performance data on critical assets to establish baseline metrics, and use these findings as a reference point for future predictive analytics as well as a benchmark for spotting abnormal patterns.
- Install IoT sensors and monitoring equipment on critical assets, and ensure that these devices are integrated correctly with your CMMS.
- Develop predictive models using the data gathered, employ machine learning algorithms to identify patterns, and leverage these findings to anticipate and prevent potential equipment failures.
- Create a system for communicating predictive insights to maintenance teams and relevant stakeholders by establishing regular reporting mechanisms and an open feedback loop.
- Conduct training sessions for maintenance personnel on how to interpret predictive data, utilize new technologies, and implement recommendations.
- Regularly review the effectiveness of the predictive maintenance program, gather feedback from maintenance teams, assess the accuracy of predictions, and refine processes as needed.
Why is Limble the best predictive maintenance solution?
Limble is widely regarded as one of the leading predictive maintenance (PdM) solutions in the market due to its user-friendly interface, robust features, and integrative capabilities. Limble’s industry-leading CMMS includes valuable features like:
- Automated preventive maintenance scheduling;
- Integrated inventory management;
- Work order management;
- Cloud-based mobile app;
- Customizable dashboards; and
- Much more.
And Limble’s Modular IoT Predictive Maintenance can help you maximize the power of your maintenance management platform with:
- Plug-and-play condition monitoring sensor setup
- Instant alerts when sensor levels reach trigger thresholds
- Automatic logging of machine monitoring data
- Scalability as your manufacturing business needs shift and grow
- Mobile access so your technicians can track and monitor real-time performance indicators from anywhere
Together, these features make Limble a top choice in the industry, both for implementing a CMMS and integrating sophisticated predictive maintenance technology. Check out the most recent G2 awards and find out what else Limble is doing to earn the recognition of its industry peers.
