Predictive Maintenance

Predictive maintenance is a strategy in which the current condition of equipment is monitored and analyzed to be able to predict equipment failure before it occurs. The main objective of predictive maintenance is to optimize maintenance scheduling and minimize equipment downtime.

Predictive maintenance uses the data collected during monitoring to determine when performance degradation or failure is likely to occur, and the severity of the decline. This allows maintenance tasks to be performed in a more accurate and cost-effective manner based on real time equipment assessment.

While the prerequisites for an effective predictive maintenance program can vary based on the organization and equipment being used, careful planning and research to establish equipment baseline conditions is always key for any successful predictive maintenance program.

Advantages of Predictive Maintenance

The advantages of predictive maintenance are numerous and wide ranging. One of the most significant of which is a decrease in ongoing maintenance costs. Predictive maintenance eliminates much of the redundancy associated with traditional maintenance methods by customizing maintenance based on current conditions and performance. Reducing the time, labor, and parts associated with pre-defined maintenance schedules can reduce maintenance costs by 50% or more.

Predictive maintenance can also significantly reduce the frequency of unexpected or catastrophic equipment failures. A worn or defective component may present signals of degradation that could potentially evade a pre-set maintenance cycle. The monitoring and analysis inherent to predictive maintenance mitigates these circumstances by creating a proactive response loop. This can lead to longer equipment life, reduced troubleshooting, and more predictable capacity planning.

Spare parts inventories can also be reduced and streamlined through predictive maintenance. Rather than stocking a comprehensive inventory of parts to address both scheduled part replacement and unscheduled failures, more parts can be sourced on an “as-needed” basis, with the predictive feedback creating a lead time buffer for part replacement.

Historical data combined with the analytical horsepower of predictive maintenance also provides indirect advantages by creating leaner operations with more visibility into equipment performance over time. Service life and mean time between failures can be more accurately predicted thus enabling more systematic capital expenditure planning. Operator safety, efficiency, and morale can also be improved by avoiding potentially dangerous and disruptive catastrophic failures.

Condition-Based Maintenance vs Predictive Maintenance

Condition-based maintenance incorporates the monitoring of physical symptoms associated with equipment and component degradation so that impending failure times can be predicted and mitigated. Condition-based maintenance often utilizes the P-F interval, the period of time between the first degradation symptoms and the failure occurrence, to proactively plan part replacement or repairs before failure occurs. The effectiveness of condition-based maintenance is influenced heavily by the consistency of these P-F intervals.

Although the term condition-based maintenance is sometimes used interchangeably with predictive maintenance, the former should be considered just one important ingredient of the latter. Predictive maintenance incorporates condition-based data, historical trends, risk factors, and additional process sensor data to form an intelligent and adaptive mix that drives the overall maintenance operation.

By analyzing signals and trends and distilling them into actions and alerts, predictive maintenance goes a step beyond condition-based maintenance by adding a layer of intelligence and prioritization to the maintenance strategy.

Preventive Maintenance vs Predictive Maintenance

Preventive maintenance, also referred to as planned maintenance, is based on pre-established time or usage intervals to determine servicing, parts replacement, or the calibration of equipment. While the frequency of preventive maintenance intervals can be adjusted based on historical trending, risk tolerance, and other factors, the maintenance tasks performed with each repetition will generally remain unchanged.

Predictive maintenance is essentially a form of preventive maintenance with a very important distinction. Rather than pre-established maintenance schedules, predictive maintenance provides adaptive maintenance intervals and service requirements based on real-time equipment assessments. Through the power of monitoring, maintenance can be performed much more efficiently, and premature replacement of viable parts can be avoided. In general, preventive maintenance is less expensive to implement than predictive maintenance, but more costly to maintain on an ongoing basis.

Studies have shown that predictive maintenance saves roughly 8% to 12% over preventive maintenance and up to 40% over reactive maintenance.

Predictive Maintenance Software

Predictive maintenance would not be possible without advanced software to gather, analyze, and store the appropriate data for each piece of equipment. A computerized maintenance management system (CMMS) is the engine behind this enhanced functionality.

Using algorithms that proactively identify trends, CMMS software can automatically generate alerts and work orders that ensure timely maintenance responses and minimize downtime risk. CMMS can add additional value as a centralized platform for maintenance history, work order management, parts inventory control, asset management, and report generation.

In many industries, equipment life is measured in decades rather than months or years. Using maintenance management software to record and maintain asset history ensures that this valuable trove of data is available for review on demand or incorporated into predictive maintenance algorithms.

Top CMMS software solutions also make use of the latest wireless technology to extend the reach of predictive maintenance. Mobile CMMS solutions allow technicians, engineers, and operators to continuously track and respond to critical maintenance issues from anywhere.

Predictive Maintenance ROI

The start-up costs associated with an effective predictive maintenance program will include software as well as sensing equipment, training, installation and other implementation expenditures. Although these deployment costs are likely to exceed that of a conventional preventive maintenance system, the return on investment can be substantial when these costs are weighed against the potential savings.

In addition to the reduction in maintenance costs, a reduction in equipment downtime of 40% or more coupled with an increase in productivity of up to 25% can facilitate a quick recoup of initial costs and an overall ROI averaging 10x across multiple industries.

Staged implementation is one potential strategy for minimizing upfront costs while still reaping the benefits of predictive maintenance. Expensive or complicated equipment, heavily utilized equipment, or equipment prone to unexpected breakdowns are some of the more logical candidates for early implementation. A staged approach also allows the organization to adapt to the new processes and technologies as they are deployed.

Predictive maintenance, with the power to substantially reduce ongoing maintenance costs as well as prevent highly disruptive and costly downtime, can undoubtedly deliver a considerable return in a relatively short period of time.