Building trust in industrial AI
Artificial Intelligence (AI) is transforming industry as a whole. It optimizes processes, reduces maintenance costs and improves operational efficiency. However, one major challenge remains : trust.
How can you be sure of the relevance of a decision if you’re not sure of the source of the information ?
Although the gains seem tempting, AI users need to understand how and why a decision was made. In industry in particular, every choice has financial and operational consequences. It can be risky to blindly follow the recommendations of an algorithm. This is why explicability and a trusted AI is essential.
The challenges of explicability and trust in industrial AI
AI guides critical operational decisions related to the following questions: should I stop my equipment? Interrupt my production cycle? Replace this part now? Is this intervention necessary? How can such a breakdown be avoided?
Those questions have an impact on operational efficiency and the potential safety of the plant. There was a time when, to guarantee this safety and minimize rebound effects, manufacturers opted for preventive maintenance (which consists of regularly replacing parts subject to wear and tear on a preventive basis).
Today, with AI, they are trying to achieve the most attractive risk-cost ratio.
But what if, for example, AI estimates the risk of failure of a critical machine in a nuclear power plant at 40%? Should we immediately shut down the equipment and intervene? What is the financial impact if the failure is not confirmed? Should operations continue while waiting for a higher confidence index, at the risk of compromising the safety of individuals?
Without a clear understanding of the results of AI, these decisions remain complex and risky. For this reason, transparency is essential. AI must be a tool at the service of decision-makers, and must therefore provide reliable, interpretable information.
Making AI explainable and trusted reduces the margin for error, facilitates adoption and improves decision-making. By understanding the origin of decisions and how reasoning is constructed, business experts can act with greater confidence.
How does DiagFit meet these challenges?
At Amiral Technologies, our Diagfit software is built around a methodology that aims to make the decisions made by models as explicable as possible, so that domain experts can appropriate the results right from the design stage and build a trusted AI.
Our approach is based on several levels of analysis at the time of model construction, enabling a better understanding of the models afterwards.
Know the data
First of all, a good understanding of the information contained in the signals coming from the equipment is key. Our experience has shown us that a good knowledge of the data upstream enables better interpretation of the results later on.
Devide the problema
A frequency filtering of the signals also enables to better isolate physical phenomena and thus refine subsequent fault detection. Combined with a sensor-by-sensor analysis, enables individual dynamics to be isolated in failure detection models.
Use sensors relations
The identification of interactions between sensors also enables the definition of groups of sensors that will allow a different level of analysis of the equipment. This multi-sensor strategy enables the identification of models adapted to each type of anomaly, and thus to refine the analysis.
Identify the origin
Finally, when an anomaly is detected, the identification of the sensors and groups of sensors that are involved, makes easier to understand the failure and locates its origin on the equipment.
Thanks to all of these features, DiagFit allows manufacturers to better understand the construction of their models and thus better appropriate the outputs of these models subsequently.
Would you like to find out how DiagFit can transform your operations ?
