As technology evolves, the old adage “Why fix something that isn’t broken” no longer holds true.
In today’s world of “always on” production, where factories and production equipment are operating 24/7, any failure results in significant production disruption that sometimes even creates a cascading effect on other downstream activities. To ensure operational reliability, proper maintenance is essential. Companies already know this. The question is therefore not to know why, but to know when. (Also read: How Digital Transformation Can Bring Resilience During Disruption.)
As organizations and operators embrace Internet of Things (IoT) technologies, including various types of robots, cameras, and sensors, the amount of data they collect will only grow.
In fact, the number of devices worldwide that are connected to each other to collect and analyze data and perform tasks autonomously is expected to almost triple, from 9.7 billion in 2020 to more than 29.4 billion. by 2030.
Such an explosive amount of data poses a challenge for humans because our brains cannot analyze the right information and process it in a timely manner. While data provides businesses with unprecedented insight into their operations, not being able to make sense of it and act on it renders that advantage obsolete.
This is where the use of predictive analytics and artificial intelligence (AI) in maintenance comes in.
What is predictive analytics?
Predictive analytics allows users to predict future trends and events with probabilities determined by historical data collected.
It forecasts potential scenarios and determines the probabilities of each, helping to guide strategic decision-making. These forecasts can be for the near future, such as predicting that a machine will break down later today, or further into the future, such as predicting the budget needed for maintenance operations for the year. Forecasts allow businesses to make better decisions and formulate strategies based on data.
Using AI for predictive maintenance
One of the most valuable features of AI is its ability to digest information from multiple sources at once, calculate the likelihood of various possible outcomes, and make recommendations based on various reasons, all without human intervention. . Such a capability allows predictive analytics to take advantage of the wealth of data available in many modern enterprises. (Also read: The main ways AI improves business productivity.)
As the world produces more and more data – whether from the thousands of IoT sensors, shipping data showing the delivery time of raw materials and parts, or open source weather data collected from weather stations around the world – AI is maturing at an ideal time to help humans make sense of all information. It can sort through the signal in a sea of noise to make actionable decisions.
With the right AI setups, companies with an ERP-integrated, AI-enabled operation can act on what they glean from the data.
How is all this taken into account in the execution of 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 life cycle of the machine. This is fine in theory, as the user can determine maintenance needs based on other similar devices. However, this is mostly theoretical, since each machine works differently depending on many factors including use, location, wear and tear. With a time-based approach, organizations run the risk of performing too much or too little maintenance on the machine.
With reactive maintenance, on the other hand, maintenance is performed when needed, which means that there will be unplanned downtime that disrupts production activities.
Predictive maintenance solves all these problems. It is a type of condition-based maintenance that monitors the condition of devices and tools through sensors that provide data used to predict when the asset will require maintenance. Therefore, maintenance is only scheduled when specific conditions are met – and before equipment begins to fail.
As AI technology matures and organizations increasingly deploy IoT tools, the use of AI-based predictive maintenance is on the rise. (Also read: What AI can do for business.)
Predictive maintenance in action
While virtually any business that operates machines that require regular maintenance can benefit from predictive maintenance (depending on the cost of machine downtime), some see greater benefits than others.
Field service companies, for example, benefit a lot from predictive maintenance due to the remote nature of their operations. With assets such as oil rigs and wind turbines located in remote, weather-sensitive locations, reacting to a failing machine can significantly disrupt production.
Worse still, performing maintenance after the fact has significant costs, as spare parts must be ordered and maintenance crews must be quickly deployed to these remote locations. With predictive analytics, however, field service organizations can perform necessary maintenance on a wind turbine part before it achieves consistent power output. (Also read: The 6 most amazing advances of AI in agriculture.)
By analyzing machine vibration, acoustics and temperature, for example, operators can uncover potential problems due to issues such as imbalance, misalignment, bearing wear, lubrication or fluid flow. looks inadequate.
Another example is an alarm serving as a fault signal/code of equipment that has failed. The system can analyze previous maintenance work performed for that type of equipment as well as that particular signal/fault code. Based on the history, the system determines the last number of times it has seen this combination (previous maintenance work and particular signal/fault code). A technician will then be dispatched at an appropriate time prior to any actual failure, armed with the applicable system-recommended spares to perform the repair. Predictive analytics allows operators to more accurately track potential machine wear and defects and, more importantly, allows them to take action before the machine fails.
By using historical trends and weather patterns, combined with information from sensors on equipment and expected supply chain delivery times, maintenance can be done preemptively in advance. The crew has more control over where and when maintenance takes place, instead of rushing to the rescue after the fact, allowing them to choose their battles.
While there is no surefire way to predict accidents, AI can get us as close to it as possible.
In the same way that residents of coastal regions might stock up on bottled water and backup batteries once a hurricane is spotted, an AI-integrated maintenance system allows companies to perform maintenance according to needs before problems manifest as real problems. (Also read: 6 no-code AI platforms accessible to SMBs.)
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