Modern dry low NOx combustors can target very low emissions levels by operating at a lean air/gas ratio. However, ultra-lean combustion is extremely susceptible to thermoacoustic combustion instabilities and Lean Blowout (LBO), which can lead to large pressure oscillations in the combustor and decreased durability of components. Conventional on-board diagnostics embedded in the Unit Control Panel (UCP) of a Gas Turbine, continuously check the health status of the combustion section at a high scan rate and raise alarms when abnormal conditions occur. While ensuring protection and control, UCP control logics may not provide precise indications on the nature of the issue and further troubleshooting, also using specific tools, is typically required. In a changing environment where Industrial Internet of Things (IIoT) is offering a chance to drive productivity and growth, online Monitoring and Diagnostic (M&D) software and services on connected units are becoming strategic to increase asset availability and reliability, as well as reducing maintenance costs. In this paper, we present a hybrid analytic, which combines physics-based and data-driven models, for the detection of Lean Blowout conditions on Gas Turbines equipped with Dry Low NOx multi-can combustion system. Regarding the data-driven model, we face a problem of classification and exploit dimensionality reduction to reduce the number of variables under consideration. During the development, different techniques are tested and benchmarked. The analytic is trained on real LBO events and finally is deployed in a production environment to process incoming on-line data acquired from monitored units. Obtained results are presented.

Processing natural language and extract relevant information in deep technical engineering domain remains an open challenge. On the other side, manufacturers of high-value assets which often deliver product services through the equipment life, supporting maintenance, spare parts management and remote monitoring and diagnostics for issues resolution, have availability of a good amount of textual data containing technical cases with a certain engineering depth. This paper presents a case study in which various Artificial Intelligence algorithms were applied to historical technical cases to extract know-how useful to help technicians in approaching new cases. Initially the work process and available data are presented; the focus is on the outbound communication delivered from the technical team to the site operators, that is structured in 3 main paragraphs: event description, technical assessment, recommended actions. The work proceeded in two parallel streams: the first concerned the analysis of event descriptions and technical assessments, aiming to detect recurring topics; the second concerned the analysis of recommended actions that technical support delivered trough years to site operators in order to create a library, which can help for enabling statistical data analysis, quality check review and being the starting point for further AI/NLP developments. A text preprocessing was applied to both streams, consisted in defining standard and domain entities / stopwords and identifying / removing them, creating acronyms and synonyms maps in order to make context disambiguation, sentence splitting for the recommended actions, and finally text lemmatization. For every text the output of the preprocess was a series of keywords. Then, unsupervised learning algorithms were applied. For this purpose, firstly, we applied feature extraction, bag of words (TF-IDF) and word embeddings (W2V, D2V, BERT), in order to transform our data from language domain into points in a n-features domain. Afterwards, different combinations of unsupervised algorithms were applied to split data into homogeneous groups, such as: LDA, K-means, Spectral, Affinity Propagation and HDBSCAN. The combinations between language modeling and clustering were evaluated using the Silhouette score and visual analysis. To validate the effectiveness, the developed NLP algorithms have been implemented into the current SW application used by technical support to perform the service. Moreover, a dedicated app to show trending topics and retrieve insightful information has been developed. An outlook of the open technical challenges and on the future perspective of NLP applications in the work process are finally delivered.