Did you know that unplanned downtimes cost around $50 billion every year? No facility would want to experience reduced operational efficiency caused by unexpected downtime and equipment failures.
Fortunately, organizations can perform predictive maintenance to prevent unplanned downtime and equipment failures. Let’s find out how.
In this blog post, we will delve into predictive maintenance, providing an in-depth detail of its examples, benefits, use cases, various types, and a comparison with preventive maintenance.
What is predictive maintenance
According to Plant Engineering’s 2018 maintenance survey, 80 percent of maintenance personnel prefer preventive maintenance. The survey also indicated an increase in the usage of predictive maintenance, rising from 47 percent to 51 percent, whereas the practice of running equipment to failure dropped from 61 percent to 57 percent.
The statistics provided are from a few years ago, but they still provide valuable insights into the evolving landscape of maintenance strategies. The survey analysis depicts that predictive maintenance helps organizations foresee when an asset needs maintenance, effectively preventing equipment failure.
This suggests that now more companies are gradually leaning towards predictive maintenance as they have realized its significance in detecting and resolving maintenance issues before equipment failures occur.
Predictive maintenance (PdM) is a type of condition-based maintenance that continuously monitors the condition of assets through sensor devices. This approach allows facilities to monitor the actual condition of an asset before determining what type of maintenance is needed.
The sensor devices provide real-time data to predict when a piece of equipment requires maintenance, thus preventing unplanned downtime and equipment failure.
Interestingly, predictive maintenance is known to be the most advanced type of maintenance compared to other maintenance methods. Contrary to time-based maintenance, which can lead the organizations to perform too much or not enough maintenance, and reactive maintenance, which resolves issues as they arise but results in unscheduled downtime, PdM helps ensure maintenance is performed when most needed and before any big issues arise.
With PdM, maintenance on an asset is scheduled only when specific conditions are met and before the asset experiences breakdown.
Explaining the predictive maintenance process
Let’s understand how predictive maintenance works. The first step in performing predictive maintenance is setting up baselines. This involves monitoring an asset’s condition and collecting data before installing sensors.
As a result, this creates a “control” for comparing any abnormalities, especially when you start collecting data for the asset’s condition. So when a piece of equipment performs beyond standard parameters, the sensors trigger your predictive maintenance protocol and alert you quickly.
Typically, a work order is created within your CMMS system and assigned to technicians, allowing them to perform necessary repairs to address the abnormality.
Types of predictive maintenance
The types of predictive maintenance are as follows:
Vibration analysis
This type of analysis for predictive maintenance is mostly performed on high-speed mechanical machines inside manufacturing plants. Since it has been around for a long time compared to other types of predictive maintenance, vibration analysis is slightly cost-effective. It helps organizations detect looseness, misalignment, imbalance, and bearing wear.
Acoustic analysis (sonic)
Acoustic analysis (sonic) for predictive maintenance is cost-effective and is used primarily for low and high-speed mechanical machines. It is quite popular among lubrication technicians as it helps them focus on proactive lubrication measures.
Acoustic analysis (ultrasonic)
While acoustic analysis (sonic) is used for proactive and predictive maintenance, acoustic analysis (ultrasonic) is only used for predictive maintenance. Since it can detect machine friction and stress sounds, it is used for electrical and mechanical equipment that emits subtler sounds. This type of analysis is arguably better at predicting imminent breakdowns than vibration or oil analysis.
Infrared analysis
Infrared analysis for predictive maintenance is not reliant on an asset’s rotational speed or loudness. Hence, it is suitable for various types of assets. It also excels when temperature indicates potential issues, making it a cost-effective tool for predictive maintenance. Moreover, it is generally used to detect issues related to cooling, airflow, and motor stress.
Benefits of predictive maintenance
According to the US Department of Energy, predictive maintenance is highly cost-effective, saving roughly 8 to 12 percent compared to preventive maintenance and up to 40 percent when compared to reactive maintenance.
In contrast to preventive maintenance, predictive maintenance ensures that a piece of equipment that needs maintenance is only shut down before imminent failure, reducing downtime and maintenance costs.
Below, we have outlined several other benefits of predictive maintenance:
Increased asset lifespan
Frequently monitoring equipment conditions and resolving minor issues before they turn into major ones can significantly increase equipment lifespan.
Enhanced maintenance efficiency
Instead of routine or scheduled maintenance, PdM ensures maintenance is performed only when needed, which leads to optimized resource utilization.
Improved spare parts management
Anticipating what spare parts might fail enables better inventory management, reducing the need for overstocking and ensuring parts are available when required.
Predictive maintenance industry use cases
Predictive maintenance technologies have already made a mark across several industries dealing in assets such as wind turbines, cash points, heat exchangers, or manufacturing robots.
Industries heavily reliant on assets including transportation, energy, telecommunications, and manufacturing, where unexpected equipment failures could have prevalent repercussions, are increasingly inclining towards advanced technologies to enhance equipment reliability and boost employee productivity.
Here are some popular industry use cases for predictive maintenance:
Energy: Frequent power outages can cause energy companies major financial losses and their customers to churn.
Manufacturing: Recurrent equipment breakdowns and unexpected downtime can substantially increase unit costs and disrupt the supply chain network.
Telecommunications: Promptly resolving telecom network issues is important for improving the quality of services, as even minor outages can impact a large customer base.
Railways: Detecting failures in brakes, points, or track deformations prevents service disruptions and ensures the safety of passengers.
Civil infrastructure: Enhanced assessment of structural integrity during inspection cycles helps minimize economic disruptions and safety issues.
Defense: The safety of military helicopters can be enhanced by receiving advanced warnings of potential catastrophic failures, such as rotor issues.
Predictive maintenance best practices
Predictive maintenance best practices have evolved with technology and vary based on the size and scope of the organization. Let’s discuss the best practices which have proved to be beneficial for organizations.
Use CMMS system
Organizations can use CMMS systems to schedule maintenance effectively, automate checklists, enable real-time monitoring, and save short and long-term costs.
Continuous training
Ongoing training benefits maintenance managers and all staff workers, enabling them to be more efficient, and versatile. Regular maintenance staff training also improves the bottom line.
Performance monitoring device
Performance monitoring modules, which are third-party devices, can be installed on running machinery to gather valuable performance data. Using these modules, organizations can effectively centralize their maintenance activities.
Expert-backed predictive maintenance plans
Organizations can perform cost-effective maintenance by engaging their experienced engineers and enabling them to create tailored predictive maintenance plans.
Analyze long-term progress
Maintenance teams can monitor growth and adapt predictive maintenance plans to optimize efficiency, reduce downtime, and decrease costs.
Categorical task prioritization
Organizations can optimize resources by prioritizing maintenance tasks based on complexity and importance.
Implement machine learning
Companies can implement machine learning protocols for precise maintenance planning, cost savings, and improved reliability.
Predictive maintenance KPIs
Key performance indicators (KPIs) are measurable goals and benchmarks used by maintenance departments to assess their performance. With the help of predictive maintenance KPIs, managers can make real-time informed decisions and evaluate how well their teams perform while executing maintenance plans and production activities.
Maintenance teams can use work order management software to monitor and analyze their KPIs and strategic goals. CMMS software equipped with advanced reporting functionality can help teams in gaining valuable insights into their KPIs, optimizing maintenance operations.
For example, Mean Time Between Failure (MTBF) is a maintenance KPI that measures the average time between two failures of a specific piece of equipment. A facility that observes a low MTBF for a particular piece of equipment immediately knows the potential underlying problems that may require a root cause analysis instead of repetitive simple fixes.
Predictive maintenance checklist
Using a PdM checklist can help ensure all essential steps have been taken to create and maintain a successful PdM program, including installation of sensors, collecting and analyzing data, and maintenance planning and scheduling.
Let’s take a look at an example of a predictive maintenance checklist, encompassing important factors to monitor while managing predictive maintenance for equipment.
Predictive Maintenance Equipment Checklist
Hardware
☑ Verify seamless integration of machine-level sensors with the CMMS
☑ Verify proper and secure installation of sensors
☑ Confirm that the sensor readings are accurate and up-to-date
☑ Verify that sensors are clean and free of debris
☑ Ensure the connection and power supply of data communication hardware such as gateways, controllers, etc.
☑ Ensure proper functioning of data communication hardware
Software
☑ Verify proper CMMS configuration to receive data from sensors seamlessly
☑ Confirm the proper operation of CMMS software
☑ Ensure timely data transmission to the CMMS
☑ Verify that the CMMS is accurately processing and analyzing the data
☑ Verify that the CMMS is creating work orders based on predictive maintenance results
☑ Check that the CMMS is accurately tracking maintenance history and costs
☑ Ensure proper integration of the CMMS with other software systems such as ERP, SCADA, etc.)
Predictive maintenance example
Consider a scenario where a facility wants to expand its maintenance operations to meet growing demand, resulting in the addition of more equipment within the facility. The maintenance team wants to focus on streamlining production flows and detecting equipment issues quickly.
To achieve this, they can use wireless vibration sensors and a robust CMMS system. Their ultimate goal is to transition from preventive to predictive maintenance for improved uptime and efficiency.
What’s the difference between predictive and preventive maintenance
Predictive maintenance and preventive maintenance are two different maintenance strategies. While predictive maintenance uses real-time data and advanced techniques to anticipate equipment issues and perform maintenance when needed, preventive maintenance depends on fixed schedules to perform maintenance tasks, which may involve some machine downtime.
To summarize, predictive maintenance is more data-driven and efficient whereas preventive maintenance focuses on predefined schedules.
Below, the table elaborates on the differences between predictive and preventive maintenance.
| Predictive maintenance | Preventive maintenance |
| Proactive | Planned and scheduled |
| Addresses potential issues before failure | Uses scheduling software for notifications |
| Focuses on asset performance, analytics, and data for machinery services | An example would be car alerts for oil changes by mileage |
| Enhances inventory efficiency by preventing parts from running to failure or being replaced before time | Offers asset performance and health indicators |
| Machine downtime is often minimal or avoided altogether | May involve machine downtime |
| Employs advanced tools such as vibration analysis, thermography, oil analysis, and data analytics | Follows maintenance checklists and procedures |
Final thoughts
Predictive maintenance is a highly beneficial strategy but its implementation can pose some challenges. Since asset-heavy industries embrace modern technology, including GPS tracking for heavy equipment, and have a holistic view of operations, it is feasible for them to implement a predictive maintenance strategy compared to others. Although the initial investment can be high, PdM comes across as an effective long-term solution, providing a significant return on investment through cost savings and enhanced asset performance.
