Predictive Maintenance: A Complete Guide

Predictive maintenance, a cornerstone of Industry 4.0, leverages advanced condition-monitoring technologies to anticipate equipment needs, ensuring timely repairs and replacements. 

Inframatrix specialises in delivering cutting-edge solutions that empower businesses to optimise operations, reduce downtime, and enhance the longevity of their critical assets. 

By implementing predictive maintenance strategies with precision, organisations can significantly lower operational costs and achieve unparalleled efficiency in asset management.

What is Predictive Maintenance?

Predictive maintenance (PdM) is a strategy that uses data analysis to identify potential equipment defects and operational anomalies, allowing for timely repairs before failures occur. 

It aims to minimise maintenance frequency, avoiding unplanned outages and unnecessary preventive maintenance costs.

The Core Concepts and Goals

The core concept is to monitor the actual operating condition of plant equipment and systems to optimise total plant operation. 

• Condition-driven: Predictive maintenance is a condition-driven preventive maintenance program, meaning that maintenance tasks are scheduled as they are required by plant equipment, based on their actual operating condition.
• Data-driven: It relies on real-time and historical data to anticipate problems before they happen. This data includes information from sensors, industrial controls, and business software.
• Optimising plant operation: The goal of predictive maintenance is to use the actual operating condition of plant equipment and systems to optimise total plant operation.
• Maximise uptime and cut costs: Predictive maintenance extends repair intervals, reduces unscheduled outages, and boosts plant availability.
• Improve overall effectiveness: Predictive maintenance can improve productivity, product quality, and the overall effectiveness of manufacturing and production plants.

How Does it Work?

PdM operates by using data analysis to identify potential equipment defects and operational anomalies, enabling timely repairs before failures occur:

• Data Collection: Data is gathered through sensors, industrial controls, and business software such as EAM and ERP systems. This data can be structured or unstructured and can come from local files, the cloud, databases, and data historians.
• Monitoring: Real-time monitoring of asset condition and performance is crucial.
• Analysis: Work order data is analysed, and MRO (Maintenance, Repair, and Operations) inventory usage is benchmarked.
• Techniques: Various techniques are used to gather information, including vibration analysis, oil analysis, thermal imaging, and equipment observation.

It is a condition-driven approach that focuses on the actual operating condition of plant equipment and systems to optimise total plant operation.

Predictive Maintenance Techniques

Predictive maintenance techniques form the backbone of this forward-thinking strategy, leveraging advanced tools and methods to monitor equipment, detect anomalies and prevent potential failures. 

By applying these techniques, organisations can ensure peak operational efficiency while extending the lifespan of critical assets:

• Thermography: This involves monitoring the infrared image of electrical switchgear, motors, and other electrical equipment to detect developing problems.
• Tribology: This involves the analysis of lubricating oil to assess the condition of machinery.
• Process Parameter Monitoring: This technique is used to quantify the operating efficiency of non-mechanical plant equipment or systems.
• Visual Inspection: Regular visual checks to identify potential problems.

When is Predictive Maintenance Suitable?

Predictive maintenance is suitable for applications that have a critical operational function and have failure modes that can be cost-effectively predicted with regular monitoring.

It is unsuitable for applications that do not serve a critical function or do not have failure modes that can be cost-effectively predicted.

Advantages of Predictive Maintenance

PdM is suitable for applications that meet specific criteria related to their operational function and the predictability of their failure modes. By leveraging real-time data and advanced analytics, organisations can unlock significant operational efficiencies and cost savings:

• Cost Savings: Reduces the time equipment is being maintained, minimises production hours lost to maintenance, and minimises the cost of spare parts. It also helps to avoid the high costs associated with reactive maintenance.
• Increased Asset Life: Regular monitoring and addressing minor issues before they become major can extend the useful life of assets.
• Optimised Maintenance: Maintenance is carried out only when required, leading to efficient use of resources.
• Better Spare Parts Management: Knowing in advance what parts might fail allows for better inventory management.
• Reduced Downtime: Minimises unplanned downtime by scheduling maintenance just before imminent failure.

Disadvantages of Predictive Maintenance

While predictive maintenance offers numerous advantages, there are also some disadvantages to consider:

• High Initial Costs: Setting up predictive maintenance requires investments in sensors, data analytics software, and IoT infrastructure.
• Complexity: Implementing predictive maintenance requires integrating different technologies and systems, analysing large datasets, and retraining personnel.
• Over-reliance on Technology: There is a risk of becoming too reliant on predictive data and ignoring other signs of equipment problems.

Comparison with Other Maintenance Strategies

PdM is a condition-driven strategy that differs significantly from other maintenance approaches like run-to-failure and preventive maintenance.

Run-to-Failure

This reactive approach involves fixing machines only after they break down. It's generally the most expensive method of maintenance management due to high spare parts inventory costs, high overtime labour costs, high machine downtime and low production availability.

Preventive Maintenance

This is a time-driven approach where maintenance tasks are scheduled based on elapsed time or hours of operation. It assumes that machines will degrade within a time frame typical of their classification. 

The drawback is that the mode of operation and system variables can affect the operating life of machinery, meaning that repairs might be unnecessary or catastrophic failures can still occur.

Predictive vs. Preventive Maintenance

PdM and preventive maintenance are both strategies aimed at reducing equipment downtime and costs, but they differ significantly in their approach and execution.

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Predictive maintenance uses advanced monitoring to address issues proactively. It involves scheduling repairs before failures occur, optimising asset performance through data-driven insights.

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Preventive maintenance, on the other hand, is planned maintenance that often utilises scheduling software and relies on checklists and manuals.

Wrapping Up

Predictive Maintenance optimises maintenance schedules and minimises downtime, making it a valuable strategy despite some challenges. Combining it with other methods creates a more efficient program.

Inframatrix enhances PdM with advanced infrared imaging, helping detect early equipment failures, improve reliability, and reduce costs for smoother operations.