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

Modelling methodologies for railway asset management

Management of railway assets incurs significant expenditure. Railway asset management modelling can predict the cost and efficacy of an asset management plan, and thus support the asset management planning process. Modelling frameworks can be used to facilitate the development of large, multi-asset, whole life cycle models which can be used to represent large sections of rail track and associated assets. This is achieved with libraries of models and tools with a high level of inter-compatibility. This research set out to support the development of modelling frameworks for railway asset management. It sought to determine the state of the art of railway asset management modelling in order to find which assets require further modelling development before they can be suitably represented in a framework’s model library. It also sought to determine the most accurate and suitable modelling methodology to base the framework upon. These aims were met by first carrying out a literature review to determine the state of the art of asset management modelling for major railway asset types. This review found Petri net models solved via Monte Carlo methods to be the most suitable modelling methodology for asset management. The level crossing asset class was chosen for the development of several models to explore the different types of Petri net model, concentrating on the computational resources required. This asset class was chosen as no asset management model was found in literature, and the diversity of the asset interactions. Literature review found several asset classes in need of further development, and some where asset management modelling may not be possible without other advances. The level crossing Petri net models developed demonstrated that computational requirements differ between the various types of Petri net. Stochastic Petri nets were found to simulate quickly, but had a high memory requirement. Coloured Petri nets were found to have the opposite requirements. A novel Petri net type, the Simple Coloured Petri net was developed to create a balance in computational cost. It was further found that complex processes such as scheduling and resource allocation can only be carried out using Coloured Petri nets due to their enhanced feature set. This work has found that further research on modelling specific asset classes is required to enable the development of a complete asset modelling library for use in a framework. If large models are to be developed, it is recommended that the Simple Coloured Petri net be used to balance computational requirements. Any models requiring complex functions should be developed using the Coloured Petri net methodology

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

Data-driven prognostics for critical electronic assemblies and electromechanical components

The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

Prognostic Reasoner based adaptive power management system for a more electric aircraft

This research work presents a novel approach that addresses the concept of an adaptive power management system design and development framed in the Prognostics and Health Monitoring(PHM) perspective of an Electrical power Generation and distribution system(EPGS).PHM algorithms were developed to detect the health status of EPGS components which can accurately predict the failures and also able to calculate the Remaining Useful Life(RUL), and in many cases reconfigure for the identified system and subsystem faults. By introducing these approach on Electrical power Management system controller, we are gaining a few minutes lead time to failures with an accurate prediction horizon on critical systems and subsystems components that may introduce catastrophic secondary damages including loss of aircraft. The warning time on critical components and related system reconfiguration must permits safe return to landing as the minimum criteria and would enhance safety. A distributed architecture has been developed for the dynamic power management for electrical distribution system by which all the electrically supplied loads can be effectively controlled.A hybrid mathematical model based on the Direct-Quadrature (d-q) axis transformation of the generator have been formulated for studying various structural and parametric faults. The different failure modes were generated by injecting faults into the electrical power system using a fault injection mechanism. The data captured during these studies have been recorded to form a “Failure Database” for electrical system. A hardware in loop experimental study were carried out to validate the power management algorithm with FPGA-DSP controller. In order to meet the reliability requirements a Tri-redundant electrical power management system based on DSP and FPGA has been develope

Modelling methodologies for railway asset management

Management of railway assets incurs significant expenditure. Railway asset management modelling can predict the cost and efficacy of an asset management plan, and thus support the asset management planning process. Modelling frameworks can be used to facilitate the development of large, multi-asset, whole life cycle models which can be used to represent large sections of rail track and associated assets. This is achieved with libraries of models and tools with a high level of inter-compatibility. This research set out to support the development of modelling frameworks for railway asset management. It sought to determine the state of the art of railway asset management modelling in order to find which assets require further modelling development before they can be suitably represented in a framework’s model library. It also sought to determine the most accurate and suitable modelling methodology to base the framework upon. These aims were met by first carrying out a literature review to determine the state of the art of asset management modelling for major railway asset types. This review found Petri net models solved via Monte Carlo methods to be the most suitable modelling methodology for asset management. The level crossing asset class was chosen for the development of several models to explore the different types of Petri net model, concentrating on the computational resources required. This asset class was chosen as no asset management model was found in literature, and the diversity of the asset interactions. Literature review found several asset classes in need of further development, and some where asset management modelling may not be possible without other advances. The level crossing Petri net models developed demonstrated that computational requirements differ between the various types of Petri net. Stochastic Petri nets were found to simulate quickly, but had a high memory requirement. Coloured Petri nets were found to have the opposite requirements. A novel Petri net type, the Simple Coloured Petri net was developed to create a balance in computational cost. It was further found that complex processes such as scheduling and resource allocation can only be carried out using Coloured Petri nets due to their enhanced feature set. This work has found that further research on modelling specific asset classes is required to enable the development of a complete asset modelling library for use in a framework. If large models are to be developed, it is recommended that the Simple Coloured Petri net be used to balance computational requirements. Any models requiring complex functions should be developed using the Coloured Petri net methodology

Sensors and Methods for Railway Signalling Equipment Monitoring

Signalling upgrade projects that have been installed in equipment rooms in the recent past have limited capability to monitor performance of certain types of external circuits. To modify the equipment rooms on the commissioned railway would prove very expensive to implement and would be unacceptable in terms of delays caused to passenger services due to re-commissioning circuits after modification, to comply with rail signalling standards. The use of magnetoresistive sensors to provide performance data on point circuit operation and point operation is investigated. The sensors are bench tested on their ability to measure current in a circuit in a non-intrusive manner. The effect of shielding on the sensor performance is tested and found to be significant. The response of the sensors with various levels of amplification produces linear responses across a range of circuit gain. The output of the sensor circuit is demonstrated for various periods of interruption of conductor current. A three-axis accelerometer is mounted on a linear actuator to demonstrate the type of output expected from similar sensors mounted on a set of points. Measurements of current in point detection circuits and acceleration forces resulting from vibration of out of tolerance mechanical assemblies can provide valuable information on performance and possible threats to safe operation of equipment. The sensors seem capable of measuring the current in a conductor with a comparatively high degree of sensitivity. There is development work required on shielding the sensor from magnetic fields other than those being measured. The accelerometer work is at a demonstration level and requires development. The future testing work with accelerometers should be at a facility where multiple point moves can be made; with the capability to introduce faults to the point mechanisms. Methods can then be developed for analysis of the vibration signatures produced by the various faults

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