Application of AI to the Different Types of Maintenance
What Can You Expect from AI in Your Corrective, Preventive, Adaptive, Evolutionary, and Perfective Maintenance Plan?
Summary
- AI and Corrective Maintenance
- AI and Preventive (or Predictive) Maintenance
- AI and Adaptive Maintenance
- AI and Evolutionary Maintenance
- AI and Perfective Maintenance
- Limitations of AI in Maintenance
Since its inception in the 1950s and 1970s, AI has continued to develop. Numerous research efforts have enabled AI to become as powerful as it is today. It wasn’t until 2020 that humanity witnessed the democratization of AI with the launch of GPT-3 by OpenAI.
As you probably know, throughout the history of artificial intelligence, many projects have emerged with the goal of applying AI techniques to maintenance management. Let’s recall that the mission of software maintenance is to ensure that IT systems operate for as long as possible under optimal conditions while remaining high-performing.
There are five types of maintenance:
- Corrective maintenance
- Preventive maintenance
- Adaptive maintenance
- Evolutionary maintenance
- Perfective maintenance
It is clear that in the early days, when we wanted to integrate AI into maintenance, the aim was indeed to replace human intelligence with artificial intelligence. The pursuit of greater efficiency remains the ultimate goal. And without doubt, AI represents the most promising technology to help us achieve this. AI improves the efficiency, reliability, and durability of application maintenance systems.
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1. AI and corrective maintenance
Corrective maintenance is deployed after an anomaly or malfunction is detected. This type of maintenance involves fixing bugs and anomalies identified within an application. The objective is to restore the software to its normal functioning state. Whether related to security or functionality, corrective maintenance ensures the software resumes proper operation.
AI provides valuable support, especially in the upstream phase of problem prediction. Indeed, AI can anticipate problems before they occur. By doing so, it helps reduce the costs associated with corrective maintenance and acts as a safeguard against underlying system malfunctions.
2. AI and preventive (or Predictive) maintenance
Predictive maintenance is based on the analysis of historical data to forecast future anomalies or technical and functional failures in applications and systems. Preventive maintenance aims to anticipate issues before they arise.
Fault and failure detection are key areas where AI excels. Through machine learning, AI builds predictive models and, much like a fortune teller, proposes probabilities of whether a breakdown might occur.
Once warned, teams can anticipate these potential failures and take preventive measures to minimize the risk of system downtime. The system’s availability is significantly improved, and user satisfaction is positively impacted, naturally…
If you’re interested in forecasting maintenance costs for a mobile app, check out our article: How to Predict the Maintenance Cost of a Mobile App?
3. AI and adaptive maintenance
Adaptive maintenance does not aim to bring changes or new features to a software. It is driven by the need to adapt the software’s technologies, policies, and rules to evolving tech contexts such as operating systems, hardware, or cloud storage.
Thanks to its intrinsic strengths, improving efficiency and precision, reducing downtime, cutting maintenance costs, and enhancing security, AI naturally supports adaptive maintenance projects. It enables more effective and accurate decision-making regarding the adaptation of software so it can continue functioning normally.
4. AI and evolutionary maintenance
We can view evolutionary maintenance as the improvement of a software or application. This type of maintenance often leads to new developments that may result in additional billing (outside of a standard maintenance contract).
In this case, changes to certain functionalities, whether altered or added, stem from end-user requests. The goal is to meet new needs that the current software cannot adequately address.
As we know, AI is at the heart of more precise and impactful digital experiences that enhance end-user engagement. With its constant analysis of user behavior, AI is an asset in rethinking digital navigation by leveraging historical data that reveals emerging specific needs.
AI does not replace human thinking but provides serious data-based insights for improving the user interface.
5. AI and perfective maintenance
The goal of perfective maintenance is to improve software by adding new functionalities it previously lacked. These enhancements may affect both the backend and the frontend. Such changes don’t necessarily originate from user requests and may not be visible in the user interface. However, perfective maintenance greatly contributes to the software’s functionality and performance.
AI, as a predictor and detector of anomalies and improvable behaviors, becomes a primary source of insight before initiating perfective maintenance actions.
We can rely on AI’s predictive and analytical capabilities (while carefully verifying interpretations) to carry out software enhancements.
Let’s wrap up with a broader look at some of the limitations of AI.
6. Limitations of AI in Maintenance
Among the inherent limitations of AI are:
- The need for a large volume of data to effectively train anomaly prediction models.
- The complexity of AI algorithms, often requiring specialists to achieve optimal results.
- AI remains artificial, not human, so it may fail to fully grasp the entire context in which the software operates.
Additional common observations include:
- AI gets it wrong 2 out of 5 times (hence the need to verify critical information),
- AI can lie.
In your pursuit of corrective, preventive, adaptive, evolutionary, and perfective maintenance, AI proves to be a major asset. Despite its exponential evolution, AI is not perfect. It is strongly recommended to validate its analyses and predictions, and to integrate human, business, and field experience feedback before making radical or structural decisions.
Bibliographic References:
Thesis: Application of Artificial Intelligence in Industrial Maintenance
