Predicative maintenance is a term which means taking action to prevent machine failure using real-time data. To understand the benefit of predicative maintenance, I’m starting off this short overview with a familiar example:
Car owners often rely on scheduled inspections and maintenance to keep their vehicles oiled, aligned, and calibrated. Inspection schedules are based on prior experience and a bit of guess-work. For example, most cars can drive between 5, 000 and 7, 500 km before needing an oil change. A pair of brake pads have a life-cycle of approximately 75, 000km. Tires typically last between 3 to 4 years or about 65, 000k – whichever comes first. These are basic guidelines, but following this replacement and inspection schedule is not a guarantee that your car will continue to run at peak performance and avoid more serious issues.
Many factors can shorten the life of a machine part. Misaligned tires can wear out in a matter of months. A lack of highway driving may impact how effectively an engine can clean itself of old oil. A bit of human error and fluid in the wrong receptacle can cause complete engine failure, costing the owner thousands of dollars.
Wouldn’t it be nice to take the guess-work out of maintenance and inspections? Some high-end vehicles already employ a range of sensors to alert drivers to changes in vehicle state such as low fluid levels. In the past decade, the range of environmental sensors available to be put into car components has skyrocketed, increased in quality, and decreased in price. Drivers enjoy benefits such as parking cameras, motion sensors, and specific engine maintenance data. These sensors, specifically those related to the mechanics of the vehicle, can alert drivers to upcoming mechanical issues. This is called predicative maintenance.
Predicative maintenance is a major benefit of the Internet of Things (IoT). The Internet of Things is a catch-all term to describe how devices of all kinds are beginning to be manufactured with built-in sensors and communicative capabilities. Most of the buzz surrounding IoT concerns itself with the consumer market and gadgets, while the real innovative strides are taking place in industries behind the scenes.
One major industry to benefit from IoT is manufacturing. Predicative Maintenance means maximizing asset availability.
In the past, maintenance professionals have integrated qualitative and quantitative techniques to prevent equipment failures and mitigate machine downtime. Predicative maintenance makes it possible to optimize machine maintenance – minimizing downtime and increasing operational potential.
According to Deloitte, poor maintenance habits reduce a plant’s productive capacity by between 5 and 20 percent. As of 2018, in the US, poor maintenance cost industrial manufacturers an estimated $50 billion dollars each year. The attitude of waiting until a part breaks to replace it is expensive and an obstacle to productivity.
Predicative maintenance uses data from a variety of sources including equipment sensors, gateways, analytics, and online visualization tools. Advanced algorithms can predict failures and alert the appropriate personnel to attend to the issue, circumventing the need for monotonous inspections and freeing up labour for other tasks.
Predicative Maintenance in Railways: Churning Out Data
Railways companies are also jumping on the IoT bandwagon, using sensors and analytics to ensure that tracks and stock cars are in pristine condition. For instance, the BNSF railways utilizes cameras, force detectors, acoustic sensors, and infrared detection to catch defects in the braking capabilities of rail-cars, increased friction in bearings, and rail curves. The data collected from these sensors paints a picture of the health of the train system and improves the safety and reliability of the system.
Predicative Maintenance and Elevators: New Heights of Innovation
Elevators are reaching new heights of innovation through sensors which keep track of open-close cycles, which floors utilize doors the most, and the amount of abuse elevator doors receive through people jamming them open. This data allows companies to quantify their maintenance schedules, with the idea that “every X number of times this door opens and closes, maintenance should be performed.” The data gathered from these new-timey elevators also allows repair professionals to diagnose problems off-site and arrive for work with the correct plan and equipment. Other than avoiding downtime and saving on maintenance costs, smart elevators offer something to give all users peace of mind: real-time notifications. If an elevator stops unexpectedly with a person trapped inside, the possibility of that person waiting hours for help to arrive is dramatically reduced. Smart-alert systems linked to IoT Platforms will ensure that the right people are informed and action can be taken immediately. Of course, thanks to preventative maintenance, the number of instances in which a person becomes trapped declines significantly.
Predicative Maintenance in Oil and Gas: Funneling Data into the Hands of Decision Makers
Oil and gas companies are at risk of massive expenses should part of a pipeline fail. Chevron, for example, has placed IoT sensors and gateways across their pipelines to help identify corrosion and damages. The sensors measure pH, CO2 and H2S, as well as pipeline diameter and thickness. Data is passed to the cloud where it is evaluated, analyzed, and utilized to make maintenance decisions.
IoT-based predicative maintenance helps optimize asset availability and take off some of the burden related to meticulous and monotonous inspections. Predicative maintenance is a benefit to consumers and industries alike, saving time and money. To find out more about sensors, IoT, predicative maintenance, or another topic – get in touch with us at email@example.com and one of our team members would be happy to chat with you.