In the dynamic world of industrial operations, maintaining equipment reliability is paramount. For any asset-intensive industry, downtime can be costly, both in terms of lost productivity and repair expenses. That's where maintenance strategies come into play.

Among these, predictive maintenance (PdM) and preventive maintenance (PM) are cornerstones, ensuring that machines operate smoothly and efficiently. In this blog post, we delve into the realm of maintenance optimization, exploring how a strategic approach can enhance reliability and asset management according to Machinery Lubrication.

Understanding Predictive Maintenance (PdM) and Preventive Maintenance (PM)

Predictive maintenance involves the use of data and analytics to predict when equipment failure might occur, allowing maintenance to be performed proactively. By monitoring key parameters such as temperature, vibration, and fluid analysis, potential issues can be identified before they escalate into major problems.

On the other hand, preventive maintenance is scheduled maintenance that is performed regularly to prevent unexpected failures. While it may not be as data-driven as predictive maintenance, PM plays a crucial role in keeping equipment in optimal condition, reducing the likelihood of breakdowns.

Reliability engineering practice generally provides us with the following three methods for creating a PM plan:

1. Reliability-Centered Maintenance (RCM) approach
2. Failure reporting and corrective action system (FRACAS) approach
3. Judgment-based approach

RCM is a systematic procedure for formulating an ideal maintenance schedule for a freshly constructed system. It entails determining the risk connected to particular failure modes, assessing the effectiveness of the controls in place, and, in the absence of historical data, assigning a risk priority number (RPN).

The process involves a failure modes and effects analysis (FMEA) worksheet, defining specific controls to mitigate risk and reduce associated RPN. RCM works, but its slowness and high cost are the main issues.

Decision matrix helps select the best PM optimization process. (Source: Machinery Lubrication)

The reliability engineer says that rather than speculating about possible failures in manufacturing plants, a FRACAS-based approach is more effective. Through the collection of failures' causes, effects, and data, an optimized maintenance plan based on real plant issues can be developed, enabling a more accurate risk assessment. When it comes to handling equipment failures, this method is more successful and efficient.

According to Machinery Lubrication, they prefer mode-by-mode assessment of failure consequences using FRACAS-based PM optimization. This approach makes use of data to assess risk profiles and measure the frequency of failures using techniques like Weibull analysis. Experienced maintenance and systems engineers may employ judgment-based techniques in the lack of data; this might be the best option if the equipment is not known to be problematic, expensive to replace or repair, or operationally critical. These methods offer insightful information about the risk profile of the apparatus.

Selecting a Strategy

When deciding on the approach, two main factors drive the decision: equipment/process criticality and the quality of FRACAS data.

The FRACAS method is used for routine maintenance tasks when system criticality is high and FRACAS data quality is high. RCM is added to the process to analyze high-impact, low-frequency-of-occurrence failure modes. Because of its dependability, FRACAS is the main method of decision support. In contrast, the RCM methodology is applied if criticality is high but there is a dearth of high-quality FRACAS data, and it makes use of inductive reasoning techniques to assess possible failures.

On the other hand, RCM is usually not used when system criticality is low. The PM optimization process is guided by historical observations when the quality of the FRACAS data is high. Optimization of PM plans is guided by judgment-based methods when criticality is low and FRACAS data quality is low.

Developing the Master Plan

In the process of establishing a reliable maintenance strategy, the next step is to construct PM plans, which we refer to as master plans, tailored to specific machine classes or sub-classes. These master plans are essentially compilations of component-level plans, curated into a cohesive PM master plan for the designated equipment category.

For instance, consider a motor-driven pump of a specific type; it necessitates its own PM master plan. However, the general maintenance for the motor remains consistent across all motor-driven systems. Therefore, the PM plan for the motor propelling the pump can be adapted for other similar motor-driven assets. Here are some key considerations when formulating component-level master plans:

1. Tailor component-level plans according to equipment demographics. For instance, with electric motors, tasks vary based on factors like system criticality, horsepower, motor type, accessibility for maintenance, bearing type, shaft orientation, and lubrication system.
2. Integrate attribute variables into the PM plan to eliminate ambiguity. Define attribute variables with acceptable range statements, specifying limits to ensure clarity in tasks like system pressure checks or belt tension verification.
3. Incorporate visual PMs and a visual plant, utilizing images to aid technicians in locating PM activity points and demonstrating acceptable vs. unacceptable conditions, particularly useful for visual inspections.
4. Translate system-level PM tasks into class or sub-class master plans, ensuring that overarching tasks are included in each category-specific master plan.
5. Establish standard parts and labor time estimates for each task to conduct a cost benefit analysis. Compare projected PM costs to current spending for the asset category; typically, projected costs are lower, resulting in both cost reduction and improved reliability.
6. Assign standard tasks to appropriate personnel categories, such as mechanical maintenance, E&I maintenance, operator/TPM care, predictive maintenance technicians, or contractors, streamlining task delegation and accountability.

An auditable trail connecting site plans, equipment plans, and master plans for each equipment class and subclass must be provided by your PMO process, which also needs to strike a balance between standardization and customization. (Source: Machinery Lubrication)

Making a Master Plan Assignment

After crafting the master plan, the next step is its assignment. In multi-plant settings, there might be site-specific adjustments required. Certain plants could have unique safety or environmental regulations not applicable across all locations, especially in multinational operations.

Similarly, task assignments might differ from site to site. Some plants may employ multi-skilled crafts or have union regulations affecting task assignments for operators. Additionally, reliance on contractors for PdM or other tasks can vary between plants.

When assigning PM master plans to specific machines, it's crucial to define attribute variables tailored to each application. Tasks may require addition, deletion, or modification to accommodate equipment design variations, operational context, or environmental conditions.

To manage these intricacies effectively, master plans should be developed within a database application offering flexibility to incorporate and track changes. This ensures an auditable trail from the master plan to site-specific plans and equipment plans, facilitating seamless adaptation and maintenance.

Unlocking the Potential of Maintenance Optimization

At CRE Philippines, we understand the challenges that organizations face in optimizing their maintenance strategies. We offer comprehensive training programs designed to equip maintenance professionals with the knowledge and skills needed to implement effective maintenance optimization strategies.

One of our program is the PM Optimization Training, which focuses on leveraging predictive maintenance techniques to enhance asset reliability and performance. Through a combination of theoretical knowledge and practical exercises, participants learn how to:

• Develop predictive maintenance schedules based on equipment condition monitoring data.
• Implement advanced predictive maintenance techniques such as vibration analysis, infrared thermography, and oil analysis.
• Integrate predictive maintenance with existing preventive maintenance programs for maximum efficiency.
• Utilize data analytics tools to identify trends and patterns in equipment performance, enabling proactive maintenance interventions.

By enrolling in our CRE PM Optimization Training program, maintenance professionals can take their skills to the next level and drive tangible improvements in equipment reliability and performance.

Contact us today to learn how we can help streamline your maintenance processes and drive operational excellence.