Predictive Maintenance: Ensuring Business Continuity with AI

As technology evolves, the old adage “why fix what isn’t broken” is no longer valid. In today’s “always on production” world, where factories and production equipment are running 24/7, any failure can severely disrupt production and sometimes even have cascading effects on other downstream businesses. To ensure operational reliability, adequate maintenance is key. Businesses already know this, so it’s not a question of why but when. As organizations and operators adopt Internet of Things (IoT) technologies, including robots, cameras and sensors of all kinds, the amount of data they collect will only continue to grow. In fact, the number of devices worldwide used to collect, analyze data, and perform tasks autonomously is expected to nearly triple from 9.7 billion in 2020 to 29.4 billion in 2030. Such an explosive amount of data is a challenge for humans because our brains cannot analyze and process the correct information in a timely manner. While data provides businesses with unprecedented insight into their operations, this advantage becomes obsolete if the data is not understood and acted upon. This is why predictive analytics and artificial intelligence (AI) are used in maintenance.

What is Predictive Analytics?

Predictive analytics allow users to predict future trends and events by determining probabilities from collected historical data. It anticipates potential scenarios and determines the likelihood of each, helping drive strategic decisions. These predictions can be near-term, such as predicting that a certain machine will break down later in the day, or more distant future, such as predicting the budget required for maintenance operations this year. Prediction enables businesses to make better decisions and develop data-driven strategies.

Predictive Maintenance Using Artificial Intelligence is One of the most valuable capabilities of artificial intelligence is its ability to simultaneously digest information from multiple sources, calculate probabilities of various possible outcomes, and make recommendations based on various reasons—all without human input. This capability enables predictive analytics to leverage the vast amounts of data available in many modern enterprises. As the world generates more and more data, whether from thousands of IoT sensors, shipping data showing when raw materials and parts are delivered, or open-source weather data collected from weather stations around the world, artificial intelligence is maturing , to help humans understand all the information. It can filter out signals from the noise and make actionable decisions. With the proper AI configuration, businesses with AI, ERP integrated operations can act on the information gleaned from the data. How does this all affect maintenance?

Currently, there are three types of maintenance: time-based maintenance, reactive maintenance, predictive maintenance. Time-based maintenance is when the user performs maintenance according to a schedule, usually the expected lifetime of the machine. This is fine in theory because users can base their maintenance needs on other similar devices. However, this is mostly theoretical, as the functionality of each machine depends on many factors including use, location, wear and more. Using a time-based approach, organizations may perform too much or not enough maintenance on machines. On the other hand, with reactive maintenance, maintenance is done when needed, which means there will be unplanned downtime, interrupting production activities. Predictive maintenance addresses all of these issues. This is condition-based maintenance, where the condition of equipment and tools is monitored through sensors, and the data provided by the sensors is used to predict when the asset will require maintenance. Therefore, maintenance is only planned when certain conditions are met, that is, before equipment starts to fail.

The use of AI-enabled predictive maintenance is increasing as AI technologies mature and organizations deploy more and more IoT tools. Implementation of predictive maintenance. While almost any business that requires regular maintenance of machinery can benefit from predictive maintenance, depending on the cost of machine downtime, some benefit more than others. For example, field service businesses benefit greatly from predictive maintenance due to the remote nature of business operations. Because assets such as oil rigs and wind turbines are located in remote locations and are vulnerable to severe weather, reactions to machine failures can severely impact production. Worse, performing maintenance after the fact comes at a huge cost, as spare parts need to be ordered and maintenance crews need to be quickly deployed to those remote locations. However, with predictive analytics, field service organizations can perform necessary maintenance on wind turbine components before they can no longer guarantee continued power generation. For example, by analyzing a machine’s vibration, acoustics, and temperature, operators can spot potential problems such as imbalance, misalignment, bearing wear, insufficient lubrication, or airflow implementation of predictive maintenance. While almost any business that requires regular maintenance of machinery can benefit from predictive maintenance, depending on the cost of machine downtime, some benefit more than others.

For example, field service businesses benefit greatly from predictive maintenance due to the remote nature of business operations. Because assets such as oil rigs and wind turbines are located in remote locations and are vulnerable to severe weather, reactions to machine failures can severely impact production. Worse, performing maintenance after the fact comes at a huge cost, as spare parts need to be ordered and maintenance crews need to be quickly deployed to those remote locations. However, with predictive analytics, field service organizations can perform necessary maintenance on wind turbine components before they can no longer guarantee continued power generation.

For example, by analyzing a machine’s vibration, acoustics, and temperature, operators can spot potential problems such as imbalance, misalignment, bearing wear, insufficient lubrication, or airflow.

Summarize

While there is no foolproof method of predicting disaster, artificial intelligence could bring us as close to it as possible. Just as people along the coast stock up on bottled water and spare batteries if they notice a hurricane, a maintenance system integrated with artificial intelligence could allow businesses to perform maintenance as needed before any problems become real problems.