There is nothing worse than unplanned downtime. Whether it’s your personal vehicle or a manufacturing plant, unplanned downtime is one of the most disruptive things that can occur delaying schedules and incurring significant costs. One of the other least favorite occurrences for someone that owns a physical asset is paying for maintenance when it’s not needed.
What if we had a dynamic solution where the “service now” light only appeared when the vehicle or asset really needed service? Assets would go longer between services; owners would spend less on annual maintenance and there would be significantly less unplanned downtime due to failures. This model is called predictive maintenance and it can be applied to more than just a vehicle—in fact, it can be applied to larger technology-driven organizations.
Organizations that invest deeply in industrial equipment – like manufacturing plants, energy and utilities and other large-scale facilities – can benefit from moving from a preventative maintenance model to a predictive maintenance model. A predictive maintenance model extends the equipment’s lifecycle, decreases maintenance costs and reduces unplanned downtime.
What is predictive maintenance?
Predictive maintenance is when a system, or model, continuously monitors the condition and performance of equipment or assets to predict when it is about to fail. This allows for the unit to be repaired or replaced prior to failure, which lowers maintenance frequency while simultaneously reducing unplanned downtime. In turn, downtime costs are reduced, and operational efficiency is optimized. A predictive maintenance model also promotes technology modernization as it allows for the seamless adoption of emerging technologies. Businesses can more accurately predict equipment failures and maintenance needs by embracing emerging technologies such as IoT sensors, mobile edge computing, artificial intelligence, and digital twins. When applied appropriately, these technologies allow a business to evolve its maintenance philosophy from reactive and proactive to predictive.
The value of predictive maintenance
Every capital-intensive business that seeks to maximize the utilization of its assets and how assets are maintained has significant improvement in its asset reliability and longevity. Most organizations today practice proactive maintenance which means more planned outages and higher maintenance costs but less unplanned downtime. Unplanned downtime can equal six percent of total runtime for some businesses, and the average manufacturer sees approximately 800 hours of equipment downtime each year. Eighty-two percent of companies have experienced at least one unplanned downtime incident over the past three years, and unplanned downtime costs industrial manufacturers as much as $50 billion annually.
Many businesses with complex operations already use IoT to monitor and manage equipment. Those devices produce volumes of data and that data can enable predictive maintenance to reduce equipment downtime and maximize equipment ROI.
A successful predictive maintenance strategy enables higher availability and increased performance. The United States Department of Energy estimates that implementing predictive maintenance can reduce downtime by 35% to 45%, reduce maintenance costs by 25% to 30%, and increase production by 20%-25%.
IoT as a basis for predictive maintenance
IoT sensors (devices that measure physical attributes such as temperature, vibrations, etc., and translate them into digital form) are foundational to more sophisticated predictive maintenance approaches because they collect the data on which those approaches are based. Businesses have been retrofitting facilities and equipment with a variety of IoT sensors for years, and most new equipment comes with sensors already built-in to measure variables like vibration, temperature, noise and power consumption. Some IoT solutions are even able perform an automated visual inspection.
Data analytics and visualization: a more insightful approach to equipment maintenance
IoT data enables data analytics and supports visualization. Data analytics and visualization deliver business insights, while dashboards provide holistic views of equipment conditions by synthesizing IoT data into actionable information.
By presenting IoT data in a cogent way, equipment problems become more discoverable and allow businesses to respond more quickly. Unfortunately, businesses remain in reactive mode as human beings must monitor dashboards and respond to the equipment condition and performance information depicted there. Could automation leveraging artificial intelligence do the monitoring and identify potential issues before they occur?
AI finds the patterns to predict maintenance needs
Applying artificial intelligence to asset-generated data allows organizations to predict failures before they occur, further reducing the need for planned maintenance and downtime while also minimizing unplanned downtime.
Like data analytics and visualization, AI consumes data from IoT devices. Unlike analytics and visualization, AI algorithms can use that data to identify future trends, detect anomalies and predict failures by analyzing performance data collected in the past.
This is the promise of predictive maintenance: extrapolating insights from the data without human intervention – and making predictions from it.
AI pulls the business out of reactive and preventative maintenance modes by generating dynamic predictions based on real-world data.
A new world of predictive maintenance: digital twins
Digital twin technology empowers businesses to create interactive virtual models of their assets based on real-world IoT sensor and performance data to achieve a new level of insights around asset utilization and performance.
Digital twins provide the freedom to explore possibilities without risk. A digital twin of a machine — or even of an entire facility — is a model of the subject in a virtual world.
Here too, IoT data provides the basis for the model. Businesses can run simulations of new maintenance strategies to identify the optimal approach to minimize planned downtime and continue to reduce unplanned breakdowns. Because the digital twin is built on information that’s continually refreshed in real-time, the model continues to evolve to emulate the asset reliability and its current condition in the real world.
Digital twins allow businesses to experiment more. The virtual re-creation of an asset is a risk-free environment to run simulations and develop scenarios that answer questions like “how many more days can we forego scheduled maintenance without a breakdown?” or “how can we extend the life of this asset?” Or even, “How much longer can I go without servicing my car?”
Businesses want to maintain equipment as frequently as necessary, but no more frequently than necessary. Digital twins help businesses find that sweet spot.
Deriving value from predictive maintenance
Not every circumstance calls for building and maintaining a digital twin, or for implementing an AI model. Many maintenance situations are effectively addressed with simpler solutions. By getting acquainted with the potential of predictive maintenance, leaders can determine what maintenance solutions are appropriate to their conditions. Businesses that use IoT devices to monitor critical assets and facility conditions will, however, benefit from a fresh look at how they use their IoT data today — and from learning how IoT data could optimize the performance and proactive maintenance of their assets. They will discover how to derive more value from IoT data they already possess — and from the equipment their IoT devices are already monitoring.