Predictive maintenance (PdM) is the process of using asset operating conditions to predict when and how a failure will occur. This typically requires the use of software, which uses asset condition data (gathered through hardware) to create graphs and reports that managers can use to make informed decisions.
This guide will help prospective buyers of predictive maintenance software determine the right solution for their business. We’ll go over:
“Predictive maintenance” is a term easiest to describe in relation to preventive maintenance methods. With preventive maintenance, work is performed based on a schedule, which is usually created using historical knowledge of how—and how often—a particular asset breaks down. According to a 2015 study by Plant Engineering Magazine, preventive maintenance methods are used by 85 percent of maintenance professionals surveyed.
But predictive maintenance goes a step beyond prevention: Using meters or sensors to track asset performance, managers can perform spot checks or stream real-time data into a computerized maintenance management software system. Service and repairs are only performed when the machinery shows signs of potential failure.
Essentially, where preventive maintenance uses educated guesses to plan maintenance schedules, predictive maintenance employs true asset conditions—in real time—to give managers the most accurate data possible for making decisions. Specialized software is used to deliver this data in an easily digestible format.
Most predictive maintenance software systems will come with some or all of the following functionality:
Ability to stream sensor data. The core promise of predictive maintenance—using live data to make informed decisions—can be achieved through occasional spot readings. However, streaming data directly into the system saves time for technicians and increases the quality of data.
Notifications and work order triggers. Predictive maintenance systems offer the ability to set parameters around asset conditions: For example, when vibration readings on a rotating piece of equipment increase beyond a user-defined range, the software can notify workers and even generate a work order to check out that asset before it fails. Some systems also allow users to define parameters based on runtime hours, temperature readings and number of units produced.
User-defined parameters that trigger notifications in Bigfoot CMMS
Reporting and data graphing. PdM systems can present streaming data in a graphical format, as well as generate reports based on the data to help identify trends or potential problems with assets.
A condition-monitoring data analysis screen from eMaint X3
Using predictive maintenance software, companies can experience several operational efficiency benefits, including:
|Reducing costly machine downtime||Using real-time data, maintenance workers can address potential problems before they occur. Thus, the most obvious benefit of PdM is keeping assets running longer and decreasing machine downtime.|
|Reducing equipment and labor costs||Since machines are running longer, costs for both repair parts and the labor required to install them are reduced. The cost of replacing a machine can also be significantly postponed.|
|Increasing production and profits.||As a result of the previous benefits, money is saved and production can increase, contributing to increased profits.|
If a company decides to implement predictive maintenance technologies, there are some hardware costs associated. A wide range of companies offer this hardware, which ranges from hand-held tools to asset-mountable sensors. Some companies offer both PdM software and the associated hardware, but more often, they are sold separately.
Costs vary, but companies don’t typically need the full array of available tools to monitor the machines they maintain. And most PdM systems are able to receive all types of condition monitoring data.
Maintenance managers can determine the types of hardware tools they need based on the equipment they maintain. Here are some common methods of monitoring assets:
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