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Predictive maintenance has revolutionized traditional, condition-based maintenance on equipment, but IoT and machine learning can further improve productivity and safety of workers.
IoT grew up with the advent of predictive maintenance, a method of monitoring and maintaining industrial equipment to reduce the likelihood of hardware failure.
Before sensors were commonly available and inexpensive, maintenance teams often completed condition-based maintenance. Teams would conduct physical inspections of critical equipment or rely on basic technologies, such as pressure meters, to determine condition. Predictive maintenance also deviated from preventive maintenance, which relies upon best practices, industry averages or waiting for equipment to break to determine when to perform maintenance.
Many organizations have made predictive maintenance systems an integral part of their business strategy. Advances in predictive maintenance technology, such as machine learning models, will likely spur further investments.
Despite changes caused by the COVID-19 pandemic that necessitated organizations reduce operation and staffing budgets, overall spending on predictive maintenance is expected to increase in the coming years. By 2022, Gartner predicted that spending on IoT-based predictive maintenance will increase to $12.9 billion, up from $3.4 billion in 2018.
What do organizations need for IoT-based predictive maintenance?
IT admins can deploy IoT sensors for predictive maintenance that monitor machinery metrics, such as vibrations, leaks and fuel levels, to detect whether equipment operates at its full potential. Sensors that use shock pulse monitoring and infrared can help with early detection of fire and toxic air pollution.
IoT sensors connect to gateways that support a variety of connection methods, including wired, Wi-Fi, cellular and low-power WAN. These gateways collect data from equipment and send it to cloud management systems, such as Microsoft Azure, IBM Cloud and RackWare Hybrid Cloud Platform.
In the cloud, organizations can use advanced analytics features to examine the wealth of information. The intelligence from this process can reduce equipment failure and create a safer working setup for employees. Computerized maintenance management system software, sometimes called enterprise asset management software, can also use this data to provide businesses with centralized work requests, automated asset tracking and reporting.
Engineers still must explore the predictive maintenance formula, notably in the area of machine learning. AI technology takes predictive maintenance a step further and prescribes potential actions to solve issues before they even happen, based on analytical insights.
For example, computer software startup Augury raised funding to grow its predictive maintenance offering for wireless sensors that record vibration, temperature and magnetism metrics from motors, compressors and pumps. An organization can use these sensors that upload the data to cloud software, which reports back on the machine's health.
IoT-based predictive maintenance applies to a variety of industries
Many organizations already use or have considered implementing predictive maintenance hardware and software across large and small worksites. Industries that benefit from the rise of predictive maintenance include the oil and gas industry, the food and beverage sector, manufacturing firms, IT services, and the energy sector.
Oil and gas companies have been at the forefront of organizations using predictive maintenance. Shell Oil, for instance, uses technology from C3.ai and Microsoft Azure cloud software to predict when compressors and valves need maintenance. Both the hardware and software can monitor equipment that steers drill bits through shale deposits.
Chemical plants in places such as such as Knoxville, Tenn., or Essen, Germany, implement sensors to reduce repair time. In these plants, tools used for maintenance often created isolated and unconnected data silos. With IoT-based predictive maintenance, the continuous flow of data to the cloud gives workers greater accuracy to identify operating conditions and errors.
The food processing industry is another major adopter of predictive maintenance. Organizations use sensors, such as infrared cameras that detect high temperatures within machinery; acoustic monitoring to root out liquid, gas or vacuum leaks within equipment; and vibration or oil analysis to ensure equipment reliability, which is a necessity in food manufacturing.