Discover how predictive maintenance technologies work, their importance for businesses, some common examples, and their applications in various industries.
Predictive maintenance technologies refer to tools used to predict equipment failures before they occur. Predictive Maintenance (PdM) technologies usually involve data analytics, sensors, detection systems, and asset maintenance management platforms to optimize equipment usability.
In relation to PdM technologies, predictive maintenance refers to the strategy of identifying possible equipment failures. Hence, predictive maintenance provides the blueprint, while PdM technologies refer to the tools used in implementing it.
How Predictive Maintenance Technology Works
PdM technologies involve a loop cycle from monitoring to the learning step as follows:
This stage relies on the use of sensors and monitoring devices to collect real-time data about the equipment. Predictive maintenance may also include asset maintenance management systems to track the condition of a company’s logistics operations. The information from these tools will be used to apprise the maintenance manager about the equipment’s condition.
Predictive maintenance technologies offer new maintenance strategies through the use of machine learning. As a result, this phase involves the use of predictive analysis to estimate the Remaining Useful Life (RUL) of an asset. Knowing an equipment’s RUL allows for effective maintenance planning, reduced downtime, and improved safety for users.
Since the sensors and monitoring devices have collected the assets’ data and the predictive analytics tools have anticipated the equipment’s RUL, the next stage involves real-time alerts and notifications.
The notifications may include early warning signs of equipment failure. Once the maintenance teams receive the real-time alerts, they can promptly check the assets to identify possible issues.
This stage refers to the actual maintenance service done at the most convenient and least disruptive time for the business. PdM enables concerned teams to schedule maintenance actions based on predicted needs, rather than responding to equipment failure.
One of the advantages of PdM technologies is its continuous refinement. For instance, maintenance results and asset histories are stored in a centralized dashboard. In effect, predictive analytics tools can use these pieces of information to enhance their accuracy regarding the organization’s equipment.
It is important to understand what technologies are used in preventive maintenance. Some of the most common examples of predictive maintenance technologies include remote monitoring sensors, asset telematics, and automated maintenance scheduling programs.
Monitoring sensors provide real-time data on different constantly changing factors to analyze equipment condition and aid in predictive maintenance. Some of the most common types of sensors used in predictive maintenance include are the following:
Temperature sensors monitor a location’s or equipment’s temperature remotely using wireless sensors for controlled environments. These sensors help organizations comply with the certifications or guidelines set by regulatory agencies.
Vibration sensors detect and measure vibrations, specifically in motors, pumps, and machinery. Vibration sensors are crucial for industries such as manufacturing, energy, and transportation, as they provide essential insights into machinery health, detect anomalies, and help prevent costly equipment failures and breakdowns.
Leak detection systems monitor the flow of liquid or gas in pipelines. They help organizations maintain safe and efficient operations by detecting leaks early.
Tank and level sensors give accurate and timely information about the contents of a tank. Through these predictive analysis tools, maintenance teams can track potential leaks, overflows, and pump issues, ensuring quick response to changes in material levels.
The technology behind asset telematics allows businesses to monitor and manage physical assets to improve their quality and lifespan. It involves the use of Global Positioning System (GPS), sensors, and network integration.
For example, a cold chain fleet management system monitors temperature and humidity levels across a fleet of refrigerated vehicles. In addition to monitoring sensors, they also use asset telematics, which ensures that vehicles and storage equipment involved in the process are working properly.
A Computerized Maintenance Management System (CMMS) streamlines maintenance operations and shows real-time information on industrial tools and equipment. CMMS also includes data on maintenance backlog management. With the help of automated scheduling programs, maintenance teams can observe data from remote monitoring sensors and asset telematics systems.
Business owners from all industries can surely benefit from the use of predictive maintenance technologies. Here are the top reasons why businesses should them:
PdM technologies anticipate equipment failures before they occur. As a result, they help avoid unplanned downtime and increased operational expenses. For example, the use of vibration sensors on massive motors and pumps can help businesses save thousands or even millions of dollars, as they can identify small issues or potential breakages before they happen or progress.
With the right sensors and systems in place, all equipment can have longer lifespans. This is supported by a recent study published on ScienceDirect, in which engineers discovered that extended equipment lifespan is one of the advantages of utilizing PdM technologies. For instance, temperature sensors can prevent equipment overheating, while leak detection systems can prevent corrosion and mold growth before they occur.
Data-driven decision-making has several benefits in itself. It offers objective insights for industry leaders and maintenance managers, enabling them to optimize business operations. Additionally, it reduces the risk of biased decision-making. For example, managers and executives can observe quantitative data from CMMS and make well-informed decisions based on it.
PdM technologies offer improved operational efficiency through increased output and reduced unplanned downtime. Moreover, they also reduce maintenance costs while maximizing equipment lifespan. These advantages help businesses yield a higher Return on Investment (ROI). Hence, predictive maintenance is one of the most effective types of maintenance.
PdM technologies have real-world applications in various industries, including manufacturing and construction. Below are some organizations that use PdM technologies for better management of their operations:
Thermosash Group is a New Zealand-based company that specializes in building envelope solutions. It utilizes remote monitoring sensors, specifically vibration sensors, to minimize the risk of unplanned maintenance at its locations.
Byblos Constructions is an Australian mining and construction company that uses an asset management system to maintain detailed service histories for its equipment. Although not a straightforward application of PdM technologies, Byblos Constructions’ use of an asset management system resulted in reduced unplanned downtime and lower vehicle maintenance costs.
Integrating improvement in practice
Discover how organizations like Toyota Material Handling, Mowi, Thermosash and Byblos Constructions are integrating improvement into how work is done to improve efficiency, reduce wastage and surface actionable insights across their operations.
As predictive maintenance technologies involve various sensors and asset management systems, it’s best to supplement their use with an operations improvement platform. These solutions can help users keep track of their devices and machinery, schedule their maintenance and inspections, and streamline workflows.
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