While the impact of IoT sensors is multifaceted, perhaps nothing is more important for modern companies than predictive maintenance tools. According to a Deloitte report, predicting enterprise asset failures can increase equipment uptime by 20%, improve productivity by 25%, and reduce failures by 70%.
In addition, the study found that predictive maintenance can reduce maintenance costs by 25%. This could be a lifesaver for many industries, raising the question: what is predictive maintenance and how does it work?
What is predictive maintenance?
The ultimate goal of predictive maintenance is to avoid costly downtime by successfully predicting when assets will fail and performing maintenance only when necessary. This requires a thorough review and analysis of data collected by environmental sensors and other IoT monitoring devices to create actionable insights and usage patterns for mission-critical equipment performance.
By its very nature, predictive maintenance improves upon reactive models, in which unplanned downtime is unavoidable. A 2015 Carbonite study estimated that downtime costs for small businesses could be as high as $427 per minute, while costs for medium to large companies soar to over $9,000 per minute.
Organizations employing time-based maintenance plans may be able to avoid unplanned downtime, but the costs of inefficient asset maintenance can also rise rapidly. The main risk is maintaining assets too frequently, leading to unnecessary expenses for replacing still-usable parts or equipment. Monitoring these same assets and maintaining them with a more efficient schedule can save up to 12% in costs compared to scheduled maintenance.
How does it work?
At its core, all predictive maintenance begins with monitoring specific conditions of the equipment. These conditions are typically based on historical performance data or equipment specifications, designed to create a range for the asset's optimal performance environment. This establishes a monitoring mechanism to compare the current condition of each asset. These conditions are observed through IoT sensors, and the data is monitored to detect any anomalous behavior that could lead to potential failures.
Predictive maintenance uses a wide variety of sensors, the most common of which are: temperature sensors, humidity sensors, motion sensors, sound sensors, light sensors, and current sensors.
Of course, simpler IoT solutions like security cameras are also an important part of predictive maintenance. Being able to observe any noticeable changes to assets from a remote location is especially valuable for maintenance work in use cases that span geographical areas, such as oil pipelines or power lines.
Artificial intelligence and Internet of Things solutions
Of course, failures do not always occur during working hours, so relying on the human eye to monitor hundreds of potential predictive maintenance data streams is usually not the most efficient approach. Therefore, developers use artificial intelligence to analyze abnormal changes in asset performance.
Artificial intelligence typically uses statistical models built from historical data to extract data from IoT sensors, runs the data against parameters identified as potential signs of degradation, and creates notifications when these conditions are met. To do this, AI creates mathematical models that encode factors such as temperature and activity into simple numerical points.
"It's really just an old-fashioned mathematical model that we've been doing for decades, the difference is that we now have the computing power to [process] massive amounts of data to find patterns," said Scott Genzer, a data scientist at RapidMiner.
at last
As the concept matures, predictive maintenance is expected to become more prevalent. A recent report by Markets and Markets predicts that the predictive maintenance market could be valued at $15.9 billion by 2026.
This concept has rapidly become a fundamental element of Industry 4.0, found in everything from the automotive industry to construction sites and oil fields. However, a MarketsandMarkets report indicates that the government and defense industries are the largest application areas for predictive maintenance.
