Predictive maintenance: Leveraging artificial intelligence to ensure business continuity

As technology evolves, the old adage “why fix something that’s not broken” is no longer valid.

In today’s “always-on” world, where factories and production equipment run around the clock, any failure can cause serious disruptions to production and sometimes even have knock-on 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 a wide variety of robots, cameras and sensors, the amount of data they collect will only continue to grow.

In fact, the number of devices used to collect, analyze data and perform tasks autonomously worldwide is expected to nearly triple from 9.7 billion units in 2020 to 29.4 billion units 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 insights into their operations, without the ability to understand and act on the data, this advantage becomes obsolete.

This is why predictive analytics and artificial intelligence (AI) are used in maintenance.

What is Predictive Analytics?

Predictive analytics allows users to predict future trends and events by determining probabilities from collected historical data.

It predicts potential situations and determines the likelihood of each situation to help drive strategic decisions. These forecasts can be near-term, such as predicting that a machine will fail later in the day, or longer-term, such as predicting the budget required for maintenance operations during the year. Forecasting enables businesses to make better decisions and develop data-based strategies.

Using Artificial Intelligence for Predictive Maintenance

One of the most valuable capabilities of artificial intelligence is its ability to digest information from multiple sources simultaneously, calculate the probabilities of various possible outcomes, and Make recommendations based on a variety of 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 it’s from thousands of IoT sensors, shipping data showing delivery times for raw materials and parts, or open source weather collected from weather stations around the world Data, artificial intelligence are maturing to help humans make sense of all information. It can filter out signals from the vast noise and make feasible decisions.

With appropriate AI configuration, enterprises with AI, ERP integrated operations can take actions based on the information gathered from the data.

How do these affect maintenance? Currently, there are three types of maintenance:

• Time-based maintenance
• Reactive maintenance
• Predictive Maintenance

Time-based maintenance refers to the user performing maintenance according to a plan, usually the expected life cycle of the machine. This is good in theory because users can determine maintenance needs based on other similar devices. However, this is mostly theoretical, as each machine's functionality depends on many factors, including use, location, wear and tear, and more. Using a time-based approach, organizations may perform too much or not enough maintenance on their machines.

On the other hand, with reactive maintenance, maintenance is performed when needed, which means there will be unplanned downtime, interrupting production activities.

Predictive maintenance solves all these problems. This is a type of condition-based maintenance that monitors the condition of equipment and tools through sensors, which provide data that is used to predict when assets require maintenance. Therefore, maintenance is only planned when certain conditions are met, that is, before the equipment starts to fail.

As AI technology matures and organizations deploy more and more IoT tools, the use of AI-powered predictive maintenance is increasing.

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 businesses benefit more than others. bigger.

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 susceptible to severe weather, the response to machine failure can severely impact production.

Worse yet, maintenance after the fact incurs huge costs, as spare parts need to be ordered and maintenance personnel need to be quickly deployed to those remote locations. However, through predictive analytics, field service agencies 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 identify potential problems such as imbalance, misalignment, bearing wear, insufficient lubrication or airflow.

Another example is an alarm, which is a signal/fault code from a malfunctioning device. The system can analyze previous maintenance work on this type of equipment, as well as specific signal/fault codes. Based on history, the system determines the last number of settings it has seen for that combination - previous maintenance work and a specific signal/fault code. Then, before any actual failure occurs, a technician will be dispatched at the appropriate time, equipped with applicable spare parts recommended by the system, to complete the repair. Predictive analytics can allow operators to more accurately track wear and potential defects on machines and, more importantly, allow them to take action before machines fail.

Preventive maintenance can be done in advance by using historical trends and weather patterns, combined with information from equipment sensors and predicted supply chain delivery times. Rather than rushing to the rescue after an incident, crews have more control over where and when repairs occur — allowing them to choose their battles.

Summary

While there is no foolproof way to predict disaster, artificial intelligence can bring us as close as possible to disaster.

Just as people along the coast might stock up on bottled water and spare batteries in the event of a hurricane, a maintenance system integrated with artificial intelligence could allow businesses to perform maintenance as needed before any issues become a real issue.

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