Today’s facilities are more complex than they have ever been, with data connections, integrated hardware, and a number of automated systems. Today’s site and asset management platforms also have complex data analysis tools that enable the use of advanced equipment maintenance methodologies. In recent years, predictive maintenance (PdM) has gotten renewed attention as monitoring tools and technologies have become more powerful. A recent survey by PwC found that 95% of respondents reported that PdM improved at least one major maintenance value driver, and 60% reported seeing a clear improvement in equipment uptime.
The concept of predictive maintenance, also called condition-based maintenance, has been around for decades as a way to detect equipment issues before they occur. This framework uses historical and real-time measurements that represent a true, quantitative data set. In this post, we’ll explore what predictive maintenance involves, how it is implemented, and why it is an important asset management solution for many businesses. A predictive maintenance program can supplement other forms of maintenance, such as preventive and corrective, to provide an alternate view for optimizing operating parameters.
One of the core features that differentiate predictive maintenance from other forms of equipment care is the use of monitoring hardware. Various monitoring components, such as facility asset management tags, LDAR tags and steam trap tags, and other asset tags or monitoring tools, are attached to each piece of equipment and connected to a centralized software system such as an Enterprise Asset Management (EAM) or Computerized Maintenance Management System (CMMS). This allows users to view important maintenance data and monitor the performance of individual components and major systems.
Monitoring can be performed with continuous (online), periodic, or remote hardware. By connecting this hardware to a piece of equipment, it is possible to measure a number of different parameters in order to assess and predict performance. Some of the most helpful measurements are:
The quality of these data inputs is critical, and it is important to regularly review the performance of these components as well as the underlying equipment. Additional technologies such as artificial intelligence (AI) and Internet of Things (IoT) devices are often used to improve the quality of data collection and analysis.
The best way for a company to adopt predictive maintenance practices is to create a formal program. This makes it easy for all relevant employees to understand how important equipment decisions will be made. Since predictive maintenance requires constant data collection and analysis, a program will also help ensure adequate resource and budget allocation. The easiest way to explain how a predictive maintenance program works is by reviewing some of the key steps:
The goal of the overall program is to create a maintenance management framework that improves decision-making and extends the useful life of the equipment. As with all forms of maintenance, careful action planning is required to find the right balance between predictive, preventive, and corrective work. Trend analysis is an important function of predictive maintenance that should be carefully understood when designing your procedures and workflows.
Predictive maintenance can be especially helpful for monitoring equipment that is expensive to repair, complex, or vulnerable to a potentially catastrophic failure. Some examples are aircraft and nuclear power plants. Predictive maintenance can be an expensive endeavor, and it also requires highly specialized skills to execute it correctly. This can be a barrier for some companies, so it’s a good idea to carefully estimate the potential ROI from a predictive maintenance program before implementation.
When properly implemented, predictive maintenance can reduce maintenance time and maximize the number of productive operating hours for equipment. It may also be possible to save costs associated with spare parts and consumables by maximizing the operating lifetime for these components. Companies should perform a thorough needs assessment and financial projection before proceeding with a formal predictive maintenance program.
Advancements in cloud computing, AI, and integrated hardware using IoT devices have made predictive maintenance a powerful tool for facility engineers and site managers. With the ability to monitor equipment in real-time and make data-based decisions, these practices can save significant costs when applied in the right situations. Proper equipment maintenance and asset management require a comprehensive approach that balances the strengths and weaknesses of many different forms of maintenance and control techniques.
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