It’s Never Too Late: Try Predictive Maintenance on a CMMS

What Is Predictive Maintenance?

Predictive maintenance (PdM) is a type of preventive maintenance strategy that uses data analytics and sensor technology to monitor the condition of equipment and predict when it is likely to fail. PdM aims to identify potential equipment failures and breakdowns before they occur. Managers can schedule maintenance activities in advance rather than waiting for equipment to fail. This scheduling avoids costly reactive maintenance or corrective maintenance caused by unplanned equipment downtime

Predictive maintenance technology includes IoT sensors and other monitoring equipment to collect data on equipment condition and performance. Managers analyze the data from this condition monitoring using machine learning algorithms or other analytics tools to identify patterns or trends that may indicate an imminent failure.

“Predictive maintenance combines the internet of things with predictive data analytics to improve the way businesses set maintenance schedules. As a result, businesses can allocate resources more efficiently, boost the longevity of machines and maximize uptime.”
Business News Daily

Companies can use predictive maintenance to monitor a range of equipment, including mechanical, electrical, and electronic systems and industrial and consumer equipment. By identifying and addressing potential equipment failures before they occur, organizations can:

This proactive maintenance strategy is sometimes referred to as condition-based maintenance.

How Predictive Maintenance Works

The goal of predictive maintenance is to use data and analytics to proactively identify and address potential equipment failures rather than reacting to them after they occur.

Predictive maintenance typically involves the following steps:

  1. First, techs equip assets with sensors and other monitoring devices to collect asset performance data. This sensor data may include vibration analysis and data from sensors such as temperature, pressure, and other factors that can indicate the health of the equipment. Simple PdM may include lubrication, belt changes, oil analysis, and fluid checks.
  2. The data collected by the sensors is transmitted to a central monitoring system. Then the data is analyzed using machine learning algorithms or other analytics tools.
  3. The analytics system looks for patterns or trends in the data that may indicate an imminent equipment failure. For example, an increase in vibration levels or temperature may indicate that a component is wearing out or is about to fail.
  4. If the analytics system detects a potential equipment failure, it will alert or notify maintenance personnel. This notification may include information about the nature of the potential failure and recommendations for how to address it.
  5. Maintenance teams can schedule maintenance activities in advance rather than waiting for the equipment to fail. This allows them to address potential equipment failures before they occur, increasing uptime and improving equipment reliability.
“Traditionally, maintenance professionals have combined quantitative and qualitative techniques to predict impending failures and mitigate downtime in their manufacturing facilities. Predictive maintenance (PdM) offers the potential to optimize maintenance tasks in real time, maximizing the useful life of equipment while avoiding disruption to operations.”
Deloitte

When to Use PdM for Maintenance

Choosing a predictive maintenance plan depends on:

  • the specific characteristics of the equipment
  • the nature of the organization’s operations, and
  • the costs associated with equipment failures and maintenance.

We recommend PdM for equipment critical to the operation of your company and that experiences frequent failures or has a high cost of downtime. It also works for equipment that is expensive to maintain or replace or that is difficult to access for maintenance.

Examples of When to Implement Predictive Maintenance

PdM strategies are practical across industries to improve equipment reliability, reduce downtime, and extend the useful life of equipment. Consider the following use cases when deciding if your company could use PdM:

  • Manufacturing: Predictive maintenance can be used to monitor and maintain manufacturing equipment, such as conveyor belts, robotics, and machine tools. Manufacturers can reduce downtime and improve production efficiency by identifying potential equipment failures in advance.
  • Industrial Machinery: Companies can use predictive maintenance to monitor and maintain industrial machines, such as pumps, motors, and compressors. Organizations can reduce downtime and improve equipment reliability by knowing in advance how each piece of equipment may fail.
  • Electrical Systems: Predictive maintenance can be used to monitor and maintain electrical systems, such as generators, transformers, and switchgear. Companies can reduce downtime and improve power reliability by identifying potential equipment failures in advance.
  • Transportation: Predictive maintenance can be used to monitor and maintain transportation equipment, such as aircraft, vehicles, and ships. Companies can reduce downtime and improve safety by identifying potential equipment failures in advance.
  • Building Systems: Predictive maintenance can be used to monitor and maintain building systems, such as HVAC systems and elevators. Companies can reduce machine downtime and improve building efficiency by identifying potential equipment failures in advance.

Examples of When NOT to Implement Predictive Maintenance

PdM depends on the specific characteristics of the equipment, the nature of your company’s operations, and the operational costs associated with equipment failures and maintenance. In other words, a predictive maintenance plan may not be suitable for equipment critical to your company’s operation or with low downtime costs.

For example, PdM may not be the best maintenance plan for:

  • Low-Cost Consumer Products: PdM may not be cost effective for low-cost consumer products, such as small appliances or consumer electronics.
  • Infrequently Used Equipment: PdM may not be necessary for equipment used infrequently.
  • Equipment with Short Useful Lifespans: PdM may be optional for equipment with a short useful life. It may less expensive to simply buy new equipment.

Integrate a CMMS into Your PdM Planning

A computerized maintenance management system (CMMS) is a maintenance software tool used to manage maintenance activities, including scheduling, tracking, and reporting on maintenance tasks. The benefits of predictive maintenance implemented via a CMMS include the following:

Asset Data Management

A CMMS can help manage real-time data collected using predictive maintenance sensors and monitoring equipment. This includes storing, organizing, and analyzing the data. By centralizing the data collection in a single system, teams can more easily access and analyze the data to identify patterns and trends that may indicate an imminent equipment failure.

Maintenance Scheduling

A CMMS can help schedule maintenance activities and work orders in advance based on the data collected by predictive maintenance sensors and other IoT (Internet of Things) devices. Teams can more easily track and prioritize tasks to ensure teams complete maintenance activities on time and correctly.

Maintenance Tracking

A CMMS is an asset management tool that tracks completed SOPs, the workers involved, and the equipment maintained. Reporting: A CMMS can generate reports on maintenance activities, including data on equipment failures, maintenance tasks, and maintenance costs. This can help teams better understand their equipment’s maintenance needs and identify improvement opportunities.

Get MaintainX for Your PDM

Using a CMMS like MaintainX with a predictive maintenance program will help you achieve significant cost savings. MaintainX can help improve maintenance efficiency and reduce unplanned downtime, emergency repairs, and inventory costs. PdM, balanced with other forms of maintenance, implemented on MaintainX can seriously help improve your maintenance key performance indicators. Check us out.

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Caroline Eisner

Caroline Eisner is a writer and editor with experience across the profit and nonprofit sectors, government, education, and financial organizations. She has held leadership positions in K16 institutions and has led large-scale digital projects, interactive websites, and a business writing consultancy.

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