321 research outputs found
Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions
The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale
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
Emerging technologies and future trends in substation automation systems for the protection, monitoring and control of electrical substations
Tese de Mestrado Integrado. Engenharia Electrotécnica e de Computadores (Automação). Faculdade de Engenharia. Universidade do Porto. 201
PreMa: Predictive Maintenance of Solenoid Valve in Real-Time at Embedded Edge-Level
In industrial process automation, sensors (pressure, temperature, etc.),
controllers, and actuators (solenoid valves, electro-mechanical relays, circuit
breakers, motors, etc.) make sure that production lines are working under the
pre-defined conditions. When these systems malfunction or sometimes completely
fail, alerts have to be generated in real-time to make sure not only production
quality is not compromised but also safety of humans and equipment is assured.
In this work, we describe the construction of a smart and real-time edge-based
electronic product called PreMa, which is basically a sensor for monitoring the
health of a Solenoid Valve (SV). PreMa is compact, low power, easy to install,
and cost effective. It has data fidelity and measurement accuracy comparable to
signals captured using high end equipment. The smart solenoid sensor runs
TinyML, a compact version of TensorFlow (a.k.a. TFLite) machine learning
framework. While fault detection inferencing is in-situ, model training uses
mobile phones to accomplish the `on-device' training. Our product evaluation
shows that the sensor is able to differentiate between the distinct types of
faults. These faults include: (a) Spool stuck (b) Spring failure and (c) Under
voltage. Furthermore, the product provides maintenance personnel, the remaining
useful life (RUL) of the SV. The RUL provides assistance to decide valve
replacement or otherwise. We perform an extensive evaluation on optimizing
metrics related to performance of the entire system (i.e. embedded platform and
the neural network model). The proposed implementation is such that, given any
electro-mechanical actuator with similar transient response to that of the SV,
the system is capable of condition monitoring, hence presenting a first of its
kind generic infrastructure
Accommodating maintenance in prognostics
Error on title page - year of award is 2021Steam turbines are an important asset of nuclear power plants, and are required to
operate reliably and efficiently. Unplanned outages have a significant impact on the
ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM)
can be used for predictive and proactive maintenance to avoid unplanned outages while
reducing operating costs and increasing the reliability and availability of the plant. In
CBM, the information gathered can be interpreted for prognostics (the prediction of
failure time or remaining useful life (RUL)).
The aim of this project was to address two areas of challenges in prognostics, the
selection of predictive technique and accommodation of post-maintenance effects, to
improve the efficacy of prognostics. The selection of an appropriate predictive algorithm
is a key activity for an effective development of prognostics. In this research, a formal
approach for the evaluation and selection of predictive techniques is developed to
facilitate a methodic selection process of predictive techniques by engineering experts.
This approach is then implemented for a case study provided by the engineering experts.
Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear
Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR)
were selected for prognostics implementation.
In this project, the knowledge of prognostics implementation is extended by including
post maintenance affects into prognostics. Maintenance aims to restore a machine into a
state where it is safe and reliable to operate while recovering the health of the machine.
However, such activities result in introduction of uncertainties that are associated with
predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy
of predictions. Therefore, such vulnerabilities must be addressed by incorporating the
information from maintenance events for accurate and reliable predictions. This thesis
presents two frameworks which are adapted for probabilistic and non-probabilistic
prognostic techniques to accommodate maintenance. Two case studies: a real-world case
study from a nuclear power plant in the UK and a synthetic case study which was
generated based on the characteristics of a real-world case study are used for the
implementation and validation of the frameworks. The results of the implementation
hold a promise for predicting remaining useful life while accommodating maintenance
repairs. Therefore, ensuring increased asset availability with higher reliability,
maintenance cost effectiveness and operational safety.Steam turbines are an important asset of nuclear power plants, and are required to
operate reliably and efficiently. Unplanned outages have a significant impact on the
ability of the plant to generate electricity. Therefore, condition-based maintenance (CBM)
can be used for predictive and proactive maintenance to avoid unplanned outages while
reducing operating costs and increasing the reliability and availability of the plant. In
CBM, the information gathered can be interpreted for prognostics (the prediction of
failure time or remaining useful life (RUL)).
The aim of this project was to address two areas of challenges in prognostics, the
selection of predictive technique and accommodation of post-maintenance effects, to
improve the efficacy of prognostics. The selection of an appropriate predictive algorithm
is a key activity for an effective development of prognostics. In this research, a formal
approach for the evaluation and selection of predictive techniques is developed to
facilitate a methodic selection process of predictive techniques by engineering experts.
This approach is then implemented for a case study provided by the engineering experts.
Therefore, as a result of formal evaluation, a probabilistic technique the Bayesian Linear
Regression (BLR) and a non-probabilistic technique the Support Vector Regression (SVR)
were selected for prognostics implementation.
In this project, the knowledge of prognostics implementation is extended by including
post maintenance affects into prognostics. Maintenance aims to restore a machine into a
state where it is safe and reliable to operate while recovering the health of the machine.
However, such activities result in introduction of uncertainties that are associated with
predictions due to deviations in degradation model. Thus, affecting accuracy and efficacy
of predictions. Therefore, such vulnerabilities must be addressed by incorporating the
information from maintenance events for accurate and reliable predictions. This thesis
presents two frameworks which are adapted for probabilistic and non-probabilistic
prognostic techniques to accommodate maintenance. Two case studies: a real-world case
study from a nuclear power plant in the UK and a synthetic case study which was
generated based on the characteristics of a real-world case study are used for the
implementation and validation of the frameworks. The results of the implementation
hold a promise for predicting remaining useful life while accommodating maintenance
repairs. Therefore, ensuring increased asset availability with higher reliability,
maintenance cost effectiveness and operational safety
Condition-based maintenance in hydroelectric plants: A systematic literature review
Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators
Improved micro-contact resistance model that considers material deformation, electron transport and thin film characteristics
This paper reports on an improved analytic model forpredicting micro-contact resistance needed for designing microelectro-mechanical systems (MEMS) switches. The originalmodel had two primary considerations: 1) contact materialdeformation (i.e. elastic, plastic, or elastic-plastic) and 2) effectivecontact area radius. The model also assumed that individual aspotswere close together and that their interactions weredependent on each other which led to using the single effective aspotcontact area model. This single effective area model wasused to determine specific electron transport regions (i.e. ballistic,quasi-ballistic, or diffusive) by comparing the effective radius andthe mean free path of an electron. Using this model required thatmicro-switch contact materials be deposited, during devicefabrication, with processes ensuring low surface roughness values(i.e. sputtered films). Sputtered thin film electric contacts,however, do not behave like bulk materials and the effects of thinfilm contacts and spreading resistance must be considered. Theimproved micro-contact resistance model accounts for the twoprimary considerations above, as well as, using thin film,sputtered, electric contact
An investigation into the prognosis of electromagnetic relays.
Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications.
Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device.
Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm.
Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made
A Literature Review of Fault Diagnosis Based on Ensemble Learning
The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance
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