An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

Big data techniques for wind turbine condition monitoring

The continual development of sensor and storage technology has led to a dramatic increase in volumes of data being captured for condition monitoring and machine health assessment. Beyond wind energy, many sectors are dealing with the same issue, and these large, complex data sets have been termed ‘Big Data’. Big Data may be defined as having three dimensions: volume, velocity, and variety. This paper discusses the application of Big Data practices for use in wind turbine condition monitoring, with reference to a deployed system capturing 2 TB of data per month

Reduction of impacts of oil and gas operations through intelligent maintenance solution

Impacts of oil and gas production operations are always very obvious when there is imbalanced operation, uncontrolled stoppage or catastrophic failure of the system during normal operations. These impacts may range from high flaring and venting of associated petroleum gas, oil release or spillage, equipment damage, fire outbreak to even fatality. Possible causes of imbalanced operations or system failure are categorised into process upset, system degradation, ineffective operation and maintenance procedures and human errors. Effective maintenance strategy integrates major components of the system; people (human factors), operation and maintenance procedures (process) and production plant (technology) to develop an intelligent maintenance solution that is capable of monitoring and detecting fault in the system at incipient stage before operational integrity is compromised. This paper deploys data-based analytics technique to develop condition-based predictive maintenance system to monitor, predict and classify performance of gas processing system. Exhaust gas temperature (EGT) of Gas Turbine Engine (GTE) is one of the operating and control parameters associated with efficiency of the GTE operation. The EGT is measured using several thermocouples, temperature sensors spaced equidistant around the circumference of the exhaust duct of the GTE. Neural network technique of multisensory data fusion is integrated with intelligent maintenance system to monitor performance of GTE, detect fault and classify performance of GTE to optimal, average and abnormal performance

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Design and Implementation of a True Decentralized Autonomous Control Architecture for Microgrids

Microgrids can serve as an integral part of the future power distribution systems. Most microgrids are currently managed by centralized controllers. There are two major concerns associated with the centralized controllers. One is that the single controller can become performance and reliability bottleneck for the entire system and its failure can bring the entire system down. The second concern is the communication delays that can degrade the system performance. As a solution, a true decentralized control architecture for microgrids is developed and presented. Distributing the control functions to local agents decreases the possibility of network congestion, and leads to the mitigation of long distance transmission of critical commands. Decentralization will also enhance the reliability of the system since the single point of failure is eliminated. In the proposed architecture, primary and secondary microgrid controls layers are combined into one physical layer. Tertiary control is performed by the controller located at the grid point of connection. Each decentralized controller is responsible of multicasting its status and local measurements, creating a general awareness of the microgrid status among all decentralized controllers. The proof-of concept implementation provides a practical evidence of the successful mitigation of the drawback of control command transmission over the network. A Failure Management Unit comprises failure detection mechanisms and a recovery algorithm is proposed and applied to a microgrid case study. Coordination between controllers during the recovery period requires low-bandwidth communications, which has no significant overhead on the communication infrastructure. The proof-of-concept of the true decentralization of microgrid control architecture is implemented using Hardware-in-the-Loop platform. The test results show a robust detection and recovery outcome during a system failure. System test results show the robustness of the proposed architecture for microgrid energy management and control scenarios

Condition-based maintenance—an extensive literature review

This paper presents an extensive literature review on the field of condition-based maintenance (CBM). The paper encompasses over 4000 contributions, analysed through bibliometric indicators and meta-analysis techniques. The review adopts Factor Analysis as a dimensionality reduction, concerning the metric of the co-citations of the papers. Four main research areas have been identified, able to delineate the research field synthetically, from theoretical foundations of CBM; (i) towards more specific implementation strategies (ii) and then specifically focusing on operational aspects related to (iii) inspection and replacement and (iv) prognosis. The data-driven bibliometric results have been combined with an interpretative research to extract both core and detailed concepts related to CBM. This combined analysis allows a critical reflection on the field and the extraction of potential future research directions

A Reinforcement Learning Approach for Transient Control of Liquid Rocket Engines

Nowadays, liquid rocket engines use closed-loop control at most near steady operating conditions. The control of the transient phases is traditionally performed in open-loop due to highly nonlinear system dynamics. This situation is unsatisfactory, in particular for reusable engines. The open-loop control system cannot provide optimal engine performance due to external disturbances or the degeneration of engine components over time. In this paper, we study a deep reinforcement learning approach for optimal control of a generic gas-generator engine's continuous start-up phase. It is shown that the learned policy can reach different steady-state operating points and convincingly adapt to changing system parameters. A quantitative comparison with carefully tuned open-loop sequences and PID controllers is included. The deep reinforcement learning controller achieves the highest performance and requires only minimal computational effort to calculate the control action, which is a big advantage over approaches that require online optimization, such as model predictive control. control