Generative AI in Plant Maintenance

5 mn read

Today almost all manufacturing verticals are highly competitive making it necessary to avoid any breakdown of the equipment in manufacturing process. This has made the past practice of Reactive Maintenance unacceptable. Aiming to eliminate breakdowns has other benefits like improved employee motivation, reduction in opportunity costs, and reduction in production cost.

There are broadly 6 maintenance types, and they are indicated in order of maturity: Ref.1

1.       Reactive Maintenance: When it breaks, you fix it. This is where most of the manufacturers start. It results in emergency maintenance which is unintentional and consists of repairing and replacing equipment on a “fire-fighting” basis. Production loss is usual result.

2.       Preventive Maintenance: You schedule replacements ahead of time before parts break, usually at a regular interval.

3.       Usage-Based Maintenance: You replace material when the machine has been used a certain amount before they break. You change oil in the equipment after say usage of 5000 hours. It doesn’t matter if it takes you one month or one year to hit five thousand hours, the oil only needs to be replaced once it has been used to its potential and further use could cause degradation of other parts.

4.       Condition-Based Maintenance: You replace the parts when they seem like they are getting too worn out to continue to function appropriately. Measurement of condition of the parts can be manual where very frequent inspections are carried out or it can be continuous by using sensors attached to the equipment. This results in more usage for the money spent.

5.       Predictive Maintenance: Utilize historical data to make predictions about when a part will break and when to replace the parts based on these predictions, prior to them breaking. This usually utilizes IIoT (Industrial IoT) and utilizes, but not always, artificial intelligence and machine learning. But it still depends on managers to take actions, like creating work order, assigning technicians etc.

6.       Prescriptive Maintenance: Advanced data analysis methods are used to do more than predict failure points, but instead provides hypothetical outcomes in order to choose the best action that can be taken – to avoid or delay failures – prior to the failure, safety hazards, and quality issues arise. It automatically creates work orders. It requires no intervention from managers and oversees equipment on its own. Generative AI (Gen AI) is helpful here.

A manufacturer implements a combination of approaches as above, based on cost-benefit analysis. The two approaches benefit significantly by utilizing AI techniques are Predictive and Prescriptive Maintenance.

The three core systems that are connected with each other are Asset Management, Maintenance Management and Inventory Management.

1.       Asset Management System (AMS): maintains map of assets deployed and their characteristics. It monitors the wear and tear, hence remaining life of assets or its parts. It sends Work Orders as triggers for maintenance requirements to Maintenance Management System.

2.       Maintenance Management System (MMS): It acts on the Work Orders from AMS to generate activity plan, inventory allocation / ordering, technician scheduling, calendar management that is required to get the job done.

3. Inventory Management System (IMS): Stores the current inventory with its parts characteristics and vendor details. On trigger from MMS, it either allocates available parts from existing inventory or gets the part through its ordering process.

The infusion of Gen AI in Plant Maintenance consists of below three key aspects:

1.       Continuous Condition Monitoring

2.       Predicting failures

3.       Executing repairs / replacements

Continuous Condition Monitoring is primarily implemented by deployment of various sensors enabled by IIoT and availability of plantwide WiFi connectivity for feeding the real time inputs as Time Series data to AMS. For example, sensors are deployed on all motors to pick rotation, speed, temperature data and send it continuously to AMS. In some cases, this could be even vision (image) data from cameras. For example, in case of monitoring depth ‘roller grooves’ used to roll steel bars from the steel billets in Steel Industry. AMS consumes all these inputs.

Predicting Failures is typically done by AMS. The data from the Continuous Monitoring is usually fed as Time Series data to AMS. Using the Machine Learning part of AI domain, the Real Time data received is analyzed using models trained by historical data of sensors, usually in correlation with different product manufacturing campaigns, to anticipate potential breakdowns in equipment or their parts. The parts’ technical data from product vendor is also used.

For example, from vendor provided data on motor, the life expectancy of motor in number of hours at certain load is known to AMS from the IMS where all details of the motor are stored. The hours of running of a motor and the load at which it was running can be found out from the readings of current drawn. The AMS may pick up all the readings of current drawn from the Time Series data it receives from sensors / meters and calculates the hours of running and average load during that period.

Similarly, for each equipment, there may be different sensor readings which can be used to calculate the used life. Applying the learning of the trained model, insights are generated to detect developing defects before they become major problems, determine the remaining usable life (RUL) of the assets. AMS then generates Work Order as request for the maintenance along with the constraints like outer time limit before the maintenance must be done.

Executing repairs  / replacements is done by MMS. Based on the maintenance request, the MMS deduces material required, skill required, and work-shift calendar carries out below activities:

  •           Receive Work Order to carry out the task and generate activity plan.
  •           Assign a technician, based on required skills, individual availability through the calendar and plant’s holiday schedule. Put that as task in the technician’s calendar.
  •           Book tools required for technician’s work.
  •           Put the request for the required material into IMS to get it allocated or purchased and then allocated.
  •           Update AMS, when job is done, with required details so that the monitoring can start again.


The amount of data available for training AI models is key to its ability to identify patterns and arriving at decisions. But obtaining large, labelled datasets can be challenging. Gen AI can be used to create new dataset matching the same underlying patterns as the original one. Such dataset can also be generated to bring in various conditions and failure scenarios that otherwise is not possible to capture with historical data alone. Availability of such large dataset ensures rigorous testing of prediction models, while mitigating bias in the model and enhancing quality of prediction.

With its ability to consume multi-modal inputs like sensor data, images and texts from manuals, camera inputs etc., it is possible to generate more comprehensive understanding of machine’s or part’s health fostering faster anomaly detection, better prediction, and accurate maintenance recommendation.

Gen AI can be used to create a Job Card and schedules for the repairs based not only on dimensions stated above, but also on analysing past performance of the technician for similar repairs, records of Mean Time Between Repairs etc.

So, Gen AI does not just predict a problem, but provides a solution. When a machine or its part shows signs of potential failure, Gen AI can look at a set of viable solutions and then generates a Work Order that ensures the most suitable fix.

No two similar machines or similar parts wear out similarly. Gen AI can generate different work orders and schedules based on real time data, corelating it with other influencing parameters. This ensures cost effectiveness in maintenance.

An interesting side effect of the ability of Gen AI to create a large set of synthetic data from a small set of actual data it its use as training tool. It can simulate a plethora of machinery failure scenarios to offer realistic training experience for technicians.


The complexity involved in deploying Gen AI in Plant Maintenance required significant computing power. The natural choice becomes usage of Cloud based infrastructure. Hence safeguarding data privacy and security becomes paramount, as it involves sensitive equipment information and maintenance logs.


Gen AI brings in lot of improvement opportunities in Plant Maintenance through greater accuracy, efficiency, and reliability. The implementation exercise should take cognizance of challenges involved to make the adoption successful.


1.       The different types of maintenance in manufacturing by Graham Immerman, MachineMetrics, 2020

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