Technology is evolving day by day and significantly impacts every field. Above all, AI is the core factor in every technological transformation. It can be said that everything on the planet benefits from AI somehow.
Predictive maintenance is one of the thousands of things that AI has transformed and brought a new color – a bright color gamut. It refers to an advanced method based on AI, machine learning, data, and analytics to predict potential machines and detect anomalies or equipment failures.
Speaking in technology terms, predictive maintenance applies technologies to improve the performance of maintenance systems. The application of artificial intelligence, machine learning, and Industrial Internet of Things (IoT) sensors to predict equipment issues makes it an essential part of modern maintenance practices.
In this article, we will share everything you need to know about AI-driven predictive maintenance, what it is, its usage, and what predictive maintenance can do for your business.
Before diving into the important aspects of AI in predictive maintenance, let’s go over the difference between preventive and predictive maintenance to better understand and avoid confusion between them.
“Periodicity” is how we describe its operating principle. Preventative maintenance occurs periodically based on the machine’s life, regardless of usage. The only variable used to predict failure is the length of time since the previous maintenance was performed.
For example, a car engine is set up to need an oil change every 1000km. Therefore, when going 1,000km, preventive maintenance will issue a notice that needs to be changed. While preventative maintenance is a significant improvement, it also has limitations. It’s basically using a very simple data model and doesn’t consider any conditions specific to your car’s engine or how the vehicle was driven.
The adjective to describe the operation of predictive maintenance is “continuous.” It continuously analyzes the status of the connected device to predict errors and update device states. The maintenance data generated during machine operation is analyzed in detail, and precise maintenance recommendations are suggested.
This data predicts the likelihood of a hazard occurring to reduce the probability of failure and provide more specific information on the cause, likelihood, and duration of failure. Accuracy also prevents unnecessary maintenance operations, which can incur costs, and limits downtime.
It can be concluded by combining the industrial Internet of Things, machine learning, and artificial intelligence. Predictive maintenance models are helping to reduce downtime, increase productivity, reduce costs, improve product design, and enhance safety for workers and businesses.
Now let’s take a look at the three main roles of AI in predictive maintenance services:
The application of AI in manufacturing and maintenance has brought about significant improvements. OEE is of indispensable necessity for evaluating the efficiency of manufacturing processes. Businesses are able to monitor device performance in real time.
For instance, machine sensors collect data on temperature, vibration, and energy consumption parameters. After that, AI algorithms will analyze the data quality to specify patterns that indicate potential malfunctions or deviations from optimal performance.
Take a beverage production line as an example. The system can detect variations in bottle filling rates through AI-enabled sensors, allowing operators to address issues promptly. As a result, maintenance teams can minimize unplanned downtime, resulting in consistent production. And overall equipment effectiveness has made a significant improvement.
TPM comes as a holistic approach involving all employees in the maintenance process — not just the maintenance team, but all employees.
AI’s role in TPM is particularly evident in its ability to facilitate data-driven decision-making. Through continuous data collected, AI systems provide insights into the device’s performance status. These systems enable businesses to adjust maintenance schedules and proactively prevent disruptions.
For instance, imagine an automobile manufacturing plant employing TPM principles with AI integration. The factory floor is equipped with AI-driven cameras to monitor assembly line activity. These cameras recognize any deviations from standard operating procedures, notifying operators in real time.
As a result, the production line remains efficient, and you can minimize defects as well as streamline the entire manufacturing process.
Traditional PPM involves default scheduled maintenance activities. That often leads to underutilization of resources and unnecessary downtime. By harnessing the predictive power of AI, businesses can turn to condition-based maintenance. This approach ensures that only necessary maintenance tasks are performed, optimizing resource allocation and minimizing disruption.
Consider a wind farm utilizing AI for PPM. Sensors installed on wind turbines continuously monitor factors such as blade conditions and generator performance. AI algorithms process this data and determine the optimal time for maintenance, reducing routine check-ups and maximizing energy production.
Beyond potential benefits, there are risks associated with AI.
Data security is a paramount concern when harnessing artificial intelligence for predictive maintenance. When IoT devices gather data on the cloud, that data is vulnerable to security problems like unauthorized access and unauthenticated manipulation. Sensitive information about equipment, processes, and operations could become vulnerable if not properly safeguarded.
Moreover, the successful implementation of AI in predictive maintenance relies heavily on integrating and aggregating diverse data sources. However, data integration can be challenging due to variations in data preparation, training data, and source data. The appropriate platform with storage capacity and advanced analytics is obligatory for effective data integration and aggregation to produce valuable insights.
Besides, AI algorithms must rely on historical data to make accurate predictions. Unfortunately, many businesses struggle to access clear, relevant, and usable historical data. Collecting historical data also requires considerable time and effort to identify data types, capture and build the right algorithms, and time to allow machine learning to work.
Legacy systems and inconsistent data collection practices can impede machine learning and AI’s ability to learn and predict effectively. Your business has to put a lot of effort into cleaning, normalizing, and transforming data to overcome this challenge.
“AI is required for effective predictive maintenance. With it, manufacturers can evaluate multiple inputs like usage, vibration, and noise directly from the machine, compare that to historical events and forecast when a machine failure is likely to occur. It then schedules the appropriate maintenance activities at the last reasonable moment before the expected failure.”
Our article somewhat helps you better understand AI-driven predictive maintenance. Technological developments like predictive maintenance are becoming more and more effective. Although challenges exist, the benefits of predictive maintenance AI are undeniable, and as technology advances, its potential continues to grow.
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