A DIAGNOSTIC & CORRECTIVE ACTION SYSTEM BASED ON DEEP LEARNING AND NATURAL LANGUAGE PROCESSING

This paper presents a novel approach to support engineers in the Remote Diagnostic Centers developed at Siemens. An innovative combination of Deep Learning 1 and Natural Language Processing (NLP) 2 technologies allows us to harness both the vast amount of the engineers’ diagnostic knowledge as well as information about the current turbine status derived from the available sensors. This approach is embedded into an overall systematic workflow building on physics-based, rule-based and datadriven methods. Based on results from a recent R&D project at Siemens Remote Diagnostics Services, we prove that this framework successfully supports engineers in identifying relevant information, thereby significantly reducing trouble shooting time and increasing both technical responsiveness capability and capacity. INTRODUCTION Both power generation and oil and gas domains operate turbo-machinery plants using complex rotating equipment. To keep up with the technological advances, the turbo-machinery industry aims to integrate engineering, manufacturing, servicing and maintenance of their plants into a single technological eco-system. The classical approaches are the utilization of condition monitoring services and diagnostic solutions, as well as the integration of design models and simulation results into the plant operations. This results in better availability and 1 Deep Learning denotes a class of machine learning methods that use multiple model layers to discover highlevel abstractions of the input data and use it for analytical tasks. Current approaches are often based on the neural network paradigm. 2 Natural Language Processing is a field of Artificial Intelligence focusing on the processing of human language by computers. For this paper, we focus on the aspect of Natural Language Understanding, i.e. the interpretation of human-generated content (here: text) by computers. reliability of plant systems, improved operations, lower maintenance cost, and higher safety. Remote monitoring and diagnostics of rotating equipment provided directly by the original equipment manufacturer (OEM) are indispensable in practice, and a focus of active research in academia. Recently, there has been an increased demand for a systematic approach to plant process safety, increased reliability and availability, lower maintenance cost, and continuous awareness about the equipment health status. This poses a challenge to the existing tool landscape, which typically relies on the adaptation of condition monitoring solutions to expert systems. Specifically, fault detection, fault isolation, failure mechanism definition and diagnosis definition as part of the systematic diagnostics are essential to support OEM engineers in their decision making process, up to and including the corrective action recommendation. However, due to the technical complexity caused by the large number of subsystems and process flows, diagnosis for industrial gas turbines is non-trivial, and requires the multi-disciplinary expertise of various engineers from domains such as system mechanics, aerodynamics, and thermodynamics, to name but a few. Specifically, root-cause analysis as part of failure analysis within system engineering & diagnostics is an indispensable feature of the design and maintenance phase. It allows identification of faults based on their causes and effects that propagate at different system levels. Consequently, a model-based approach to failure analysis for industrial gas turbine applications is used to realize an efficient system analysis. Over the last decades, a natural evolution of the systematic approach can be observed, which can be summarized as an extension of the original (1) physics-based methods by (2) rule-based systems 3 , and over the last years additionally utilization of the (3) data3 Systems utilizing (typically hand-crafted) IF-THEN rules to derive new information from known facts.