Why Predictive Maintenance Is Incomplete Without Explainable AI

We recently posted an article about 10 Common Misconceptions About Predictive Maintenance where a lot of false assumptions around predictive maintenance were clarified. Predictive maintenance brings to the table the power of Artificial Intelligence and automation. A predictive maintenance solution uses AI to analyse data and forecast maintenance needs, giving industries that rely on machinery advanced warnings to prevent downtime.

But is AI reliable? This is a question most businesses ask when it comes to implementing predictive maintenance.

The solution predicts a potential failure, and the maintenance team may not be able to ascertain from a manual inspection if there will, in fact, be a breakdown. The maintenance tasks that result from predictive maintenance are time, effort, and cost-consuming, so the accuracy of these results is extremely important.

AI analyses data to spot anomalies in patterns and predict a result, but does not effectively ‘explain’ what it did and why it did it. This essentially means the maintenance team runs maintenance tasks based on trust in the AI system’s capabilities. 

Taking a step back, it’s important to understand that AI is not a magic bullet. A predictive maintenance system uses sensors to monitor parameters, like the vibrations of a rotating gear, for example, and pick up on any irregularities to predict fault. This irregularity in vibration could have been picked up by a member of the maintenance team during a manual inspection as well.

However, an AI system trumps manual inspection in two ways – precision and scale. Modern sensors can pick up parameters at a granular level, a level that would not be noticeable by a human. While a human can inspect one part of one machine at a time, the AI system can monitor all machines simultaneously.

But the question still remains, are the results generated by the AI-enabled predictive maintenance system reliable. Is the maintenance team able to understand what the AI system did and how it came to a particular conclusion?

That brings us to Explainable AI.

What Is Explainable AI?

Explainable AI (XAI) is AI that ‘explains’ through data why and how it reached a conclusion, giving users a complete understanding not only of the results but also the path to the result. This eliminates any questions like why do this and not something elsehow accurate is this resultis this the best solution to this problemis there even a problem, etc. Explainable AI makes the system more reliable and believable. 

Explainable AI reveals, through data:

  • What has been done?
  • What is being done right now?
  • What will be done next?
  • What information are all the above actions and decisions based on?

With this information, a user can:

  • Confirm existing knowledge.
  • Challenge existing knowledge.
  • Generate new assumptions and scenarios.

In simple words, explainable AI makes the AI solution more understandable to the user and thus, makes it easier for results to be interpreted and believed. In the context of predictive maintenance, it gives enough proof to justify a maintenance task.

Explainable AI in Predictive Maintenance

Now that both predictive maintenance and explainable AI are better understood, you will better comprehend the need for explainable AI in the predictive maintenance solution you choose. 

Explainability is an issue for decision-makers who rely on the predictive maintenance solution to plan maintenance activities. Getting stakeholders to sign off requires explaining why the maintenance task is needed, and if AI isn’t providing these answers, the user has no other means of knowing. Maintenance tasks can also be automated – the predictive maintenance solution can be configured to schedule maintenance tasks as soon as parameter thresholds are crossed.

Justifying why time, effort, and cost-consuming tasks are being run requires more than just belief in the AI system. Explainable AI proves to be a solution by giving decision-makers the information they need to confirm the results are accurate and the ensuing activities are required.

Here are some questions that explainable AI answers, making the system reliable:

1. Which Parameters Influence Machine Failure?

While alerting about the probability and potential time for failure, explainable AI also presents data on the parameters that were measured in coming to this conclusion and their values. If the right parameters have maximum impact, decision-makers can immediately trust the results.

2. What More Can We Know About These Parameters?

Users can drill down on these parameters that influence machine failure to get more information, information like the importance of the parameter, impact probability, impact value, the correlation between the parameter and failure, etc.

3. How Will Changes in Individual Parameters Affect the Outcome?

Explainable AI allows users to generate scenarios in order to measure the impact of one parameter while keeping the rest constant. This will enable decision-makers to gauge the urgency for maintenance and the potential impact of failure.

4. Why Will a Particular Component of a Particular Machine Fail?

Explainable AI has the power to provide granular information for isolated components (that are being monitored) of machines. Being able to drill down to get component-specific information helps decision-makers justify the repair or replacement of a part.

This level of in-depth information helps maintenance teams understand the results presented by the AI system. It also helps them explain the need for maintenance tasks to stakeholders.

Eugenie’s Predictive Maintenance Solution With Explainable AI

The most important benefit of explainable AI, we believe, is that it makes decision-making the joint effort between AI and humans. We at Eugenie call this human-centric AI. Explainable AI allows the predictive maintenance solution and its users to ‘talk’ to each other, understand the reasons for results,, and generate scenarios for validation and prediction.

We took into account the need for users of predictive maintenance solutions to understand data easily, quickly, and completely. Our explainable AI-enabled predictive maintenance solution does exactly that – it paints the complete picture so you can interpret data correctly and trust the results.

Downtime is expensive and can cost a company an average of $2 million in losses over three years. The solution to preventing unscheduled downtime is a predictive maintenance solution. To ensure the solution gives you results you can rely on, you must select one that includes explainable AI, like Eugenie.

To learn more about how Eugenie has helped various industrial companies across the globe ensure a profitable transition to predictive maintenance, talk to us today or reach out to us at .

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