TECHNOLOGY Industrial AI’s capacity to analyze very large amounts of high-dimensional data can change the current maintenance paradigm and shift from preventive maintenance systems to new levels. The key challenge, however, is operationalizing predictive maintenance, and this is much more than connecting assets to an AI platform, streaming data, and analyzing those data. By integrating conventional data such as vibration, current or temperature with unconventional additional data, such as audio and image data, including relatively cheap transducers such as microphones and cameras, Industrial AI can enhance or even replace more traditional methods. AI’s ability to predict failures and allow planned interventions can be used to reduce downtime and operating costs while improving production yield. For example, AI can extend the life of an asset beyond what is possible using traditional analytics techniques by combining data information from designer and manufacturer, maintenance history, and Internet of Things (IoT) sensor data from end users, such as anomaly detection in engine-vibration data, images and video of engine condition. This information fusion during the lifecycle of the asset is called product lifecycle management (PLM). Explainable AI in Maintenance Advances in AI for maintenance analytics are often tied to advances in statistical techniques. These tend to be extremely complex, leveraging vast amounts of data and complex algorithms to identify patterns and make predictions. This complexity, coupled with the statistical nature of the relationships between input data that the asset provides, makes them difficult to understand, even for expert users, including the system developers, Figure 4. This makes explainability a major concern. Figure 4: Engineers and data scientist must co-create the AI solution for maintenance together While increasing the explainability of AI systems can be beneficial for many reasons, there are challenges in implementing explainable AI. Different users require different forms of explanation in different contexts, and different contexts give rise to different needs. To understand how an AI system works in the maintenance domain, users might wish to know which data the system is using, the provenance of those data, and why they were selected; how the model and prediction work, and which factors influence a maintenance decision; and why a particular output is obtained. To understand what type of explanation is necessary, careful stakeholder engage- ment and well-thought-out system design are both necessary as can be seen in figure 5. Figure 5: Architecture of explainable AI systems for Maintenance Decisions There are various approaches to creating interpretable systems. Some AI is interpretable by design; these systems tend to be kept relatively simple. An issue with them is that they cannot get as much customization from vast amounts of data as more complex techniques, such as deep learning. This creates a performance-accuracy trade-off in some settings, and the systems might not be desirable for those applications where high accuracy is prized. In other words, maintainers must accept more black boxes. In some AI systems – especially those using personal data or those where proprietary information is at stake – the demand for explainability may interact with concerns about privacy. In areas such as healthcare and finance, for example, an AI system might be analyzing sensitive personal data to make a decision or recommendation. In determining the type of explainability that is desirable in these cases, organizations using AI will need to take into account the extent to which different forms of transparency might result in the release of sensitive insights about individuals or expose vulnerable groups to harm. In the area of maintenance, when the AI recommends a maintenance decision, decision makers need to understand the underlying reason. Maintenance analytics developers need to understand what fault features in the input data are guiding the algorithm before accepting auto-generated diagnosis reports, and the maintenance engineer needs to understand which abnormal phenomena are captured by the inference algorithm before following the repair recommendations. One of the proposed benefits of increasing the explainability of AI systems is increased trust in the system. If maintainers understand what led to an AI-generated decision or recommendation, they will be more confident in its outputs. But the link between explanations and trust is complex. If a system produces convincing but misleading explanations, users might develop a false sense of confidence or understanding. They might have too much confidence in the effectiveness or safety of systems, without such confidence being justified. Explanations might help increase trust in the short term, but they do not necessarily help create systems that generate trustworthy outputs or ensure that those deploying the system make trustworthy claims about its capabilities. REFERENCES: Galar, Diego, Pasquale Daponte, and Uday Kumar. Handbook of Industry 4.0 and SMART Systems. CRC Press, 2019. Galar, Diego. Artificial intelligence tools: decision support systems in condition monitoring and diagnosis. Crc Press, 2015. Galar, Diego, Uday Kumar, and Dammika Seneviratne. Robots, Drones, UAVs and UGVs for Operation and Maintenance. CRC Press, <strong>2020</strong>. 50 maintworld 1/<strong>2020</strong>
VIBRATION ANALYSIS THERMAL IMAGING ULTRASOUND MEASUREMENT eyesight – hearing – sensitivity we have in common MASTER THE LANGUAGE OF YOUR MACHINERY WWW.ADASH.COM
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