This paper introduces an integrated system designed to enhance the explainability of fault diagnostics in complex systems, such as nuclear power plants, where operator understanding is critical for informed decision-making. By combining a physics-based diagnostic tool with a Large Language Model, we offer a novel solution that not only identifies faults but also provides clear, understandable explanations of their causes and implications. The system's efficacy is demonstrated through application to a molten salt facility, showcasing its ability to elucidate the connections between diagnosed faults and sensor data, answer operator queries, and evaluate historical sensor anomalies. Our approach underscores the importance of merging model-based diagnostics with advanced AI to improve the reliability and transparency of autonomous systems.

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the gearbox. Although domain adaptation and data synthesis methods have been proposed to overcome such domain shifts, they are often not directly applicable in real-world situations where only healthy data is available in the target domain. To tackle the challenge of extreme domain shift scenarios where only healthy data is available in the target domain, this paper proposes two novel domain knowledge-informed data synthesis methods utilizing the health data map (HDMap). The two proposed approaches are referred to as scaled CutPaste and FaultPaste. The HDMap is used to physically represent the vibration signal of the planetary gearbox as an image-like matrix, allowing for visualization of fault-related features. CutPaste and FaultPaste are then applied to generate faulty samples based on the healthy data in the target domain, using domain knowledge and fault signatures extracted from the source domain, respectively. In addition to generating realistic faults, the proposed methods introduce scaling of fault signatures for controlled synthesis of faults with various severity levels. A case study is conducted on a planetary gearbox testbed to evaluate the proposed approaches. The results show that the proposed methods are capable of accurately diagnosing faults, even in cases of extreme domain shift, and can estimate the severity of faults that have not been previously observed in the target domain.

New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault classes represented in the two domains are not the same. To enable a better transferability of the trained models between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN. The main contribution of the proposed framework is the controlled synthetic fault data generation with two main distinct characteristics. Firstly, the proposed methodology enables to generate unobserved fault types in the target domain by having only access to the healthy samples in the target domain and faulty samples in the source domain. Secondly, the fault generation can be controlled to precisely generate distinct fault types and fault severity levels. The proposed method is especially suited in extreme domain adaption settings that are particularly relevant in the context of complex and safety-critical systems, where only one class is shared between the two domains. We evaluate the proposed framework on Partial as well as Open-Partial domain adaptation tasks on two bearing fault diagnostics case studies. Our experiments conducted in different label space settings showcase the versatility of the proposed framework. The proposed methodology provided superior results compared to other methods given large domain gaps.

"Changes to Azure services and the Azure platform itself are both inevitable and beneficial, to ensure continuous delivery of updates, new features, and security enhancements. However, change is also a primary cause of service regressions that can contribute towards reliability issues--for hyperscale cloud providers, indeed for any IT service provider. As such, it is critical to catch any such problems as early as possible during the development and deployment rollout, to minimize any impact on the customer experience. As part of our ongoing Advancing Reliability blog series, today I've asked Principal Program Manager Jian Zhang from our AIOps team to introduce how we're increasingly leveraging machine learning to de-risk these changes, ultimately to improve the reliability of Azure."--Mark This post includes contributions from Principal Data Scientists Ken Hsieh and Ze Li, Principal Data Scientist Manager Yingnong Dang, and Partner Group Software Engineering Manager Murali Chintalapati.

With the explosive growth of multivariate time-series data, failure (event) analysis has gained widespread applications. A primary goal for failure analysis is to identify the fault signature, i.e., the unique feature pattern to distinguish failure events. However, the complex nature of multivariate time-series data brings challenge in the detection of fault signature. Given a time series from a failure event, the fault signature and the onset of failure are not necessarily adjacent, and the interval between the signature and failure is usually unknown. The uncertainty of such interval causes the uncertainty in labeling timestamps, thus makes it inapplicable to directly employ any standard supervised algorithms in signature detection. To address this problem, we present a novel directional label rectification model which identifies the fault-relevant timestamps and features in a simultaneous approach. Different from previous graph-based label propagation models using fixed graph, we propose to learn an adaptive graph which is optimal for the label rectification process. We conduct extensive experiments on both synthetic and real world datasets and illustrate the advantage of our model in both effectiveness and efficiency.