A diffuse reflectance spectroscopy-based method to score fibrosis in paraffin-preserved human liver specimens has been developed and is reported here. Paraffin blocks containing human liver tissue were collected from the General Hospital of Mexico and included in the study with the patients' written consent. The score of liver fibrosis was determined in each sample by two experienced pathologists in a single-blind fashion. Spectral measurements were acquired at 450-750 nm by establishing surface contact between the optical probe and the preserved tissue. According to the histological evaluation, four liver samples showed no evidence of fibrosis and were categorized as F0, four hepatic specimens exhibited an initial degree of fibrosis (F1-F2), five liver specimens showed a severe degree of fibrosis (F3), and six samples exhibited cirrhosis (F4). The human liver tissue showed a characteristic diffuse reflectance spectrum associated with the progressive stages of fibrosis. In the F0 liver samples, the diffuse reflection intensity gradually increased in the wavelength range of 450-750 nm. In contrast, the F1-F2, F3, and F4 specimens showed corresponding 1.5-, 2-, and 5.5-fold decreases in the intensity of diffuse reflectance compared to the F0 liver specimens. At 650 nm, all the stages of liver fibrosis were clearly distinguished from each other with high sensitivity and specificity (92-100%). To our knowledge, this is the first study reporting a distinctive diffuse reflectance spectrum for each stage of fibrosis in paraffin-preserved human liver specimens. These results suggest that diffuse reflectance spectroscopy may represent a complementary tool to liver biopsy for grading fibrosis.
Abstract-Within power generation, ageing assets and an emphasis on more efficient operation of power systems and improved maintenance decision methods has led to a growing focus on asset prognostics. The main challenge facing the implementation of successful asset prognostics in power generation is the lack of available run-to-failure data. This paper proposes to overcome this issue by use of full scope high fidelity simulators to generate the run-to-failure data required. From this simulated failure data a similarity based prognostic approach is developed for estimating the Remaining Useful Life of a valve asset. Case study data is generated by initializing prebuilt industrial failure models within a 970MW Pressurized Water Reactor simulation. Such full scope high fidelity simulators are mainly operated for training purposes, allowing personnel to gain experience of standard operation as well as failures within a safe, simulated operating environment. This research repurposes such a high fidelity simulator to generate the type of data and affects that would be produced in the event of a fault. The fault scenario is then run multiple times to generate a library of failure events. This library of events was then split into training and test batches for building the prognostic model. Results are presented and conclusions drawn about the success of the technique and the use of high fidelity simulators in this manner.Index Terms-Model-based prognostics, remaining useful life, high fidelity simulation, power generation,
In critical infrastructure, such as nuclear power generation, constituent assets are continually monitored to ensure reliable service delivery through pre-empting operational abnormalities. Currently, engineers analyse this condition monitoring data manually using a predefined diagnostic process, however, rules used by the engineers to perform this analysis are often subjective and therefore it can be difficult to implement these in a rule-based diagnostic system. Knowledge elicitation is a crucial component in the transfer of the engineer's expert knowledge into a format suitable to be encoded into a knowledge-based system. Methods currently used to perform this include structured interviews, observation of the domain expert, and questionnaires. However, these are extremely time-consuming approaches, therefore a significant amount of research has been undertaken in an attempt to reduce this. This paper presents an approach to reduce the time associated with the knowledge elicitation process for the development of industrial fault diagnostic systems. Symbolic representation of the engineer's knowledge is used to create a common language that can easily be communicated with the domain experts but also be formalised as the rules for a rule-based diagnostic system. This approach is then applied to a case study based on rotating plant fault diagnosis, specifically boiler feed pumps for a nuclear power station. The results show that using this approach it is possible to quickly develop a system that can accurately detect various types of faults in boiler feed pumps.
Plant fault detection and diagnosis is an increasing operational necessity, especially in the nuclear sector where safety is of the utmost importance. Currently, operators have to manually inspect data acquired across multiple assets using predefined diagnostic processes, placing a high time burden on the analyst. Data-driven approaches to solving this problem can produce accurate results approaching what the analysts can achieve but in a fraction of the time. However, the majority of these techniques are black box in nature and therefore lack the explicability, often required for critical assets in the nuclear industry. Knowledge-based systems can be used for a variety of applications to provide not only accurate decisions but also the explanation and reasoning behind these decisions. However, the knowledge elicitation process places a significant time cost associated with the development of knowledge-based systems.In this paper, an approach is proposed for the development of knowledge-based systems that allow for accurate knowledge capture and formalisation that forgo formal knowledge elicitation sessions. By firstly producing a symbolic representation of the time-series data, abstracting similar trends to produce a list of potential rules, it was found that there was a significant time saving using this approach without equivalent loss of accuracy. A knowledge-based system developed in this way would allow for accurate and transparent fault diagnosis in any discipline, without placing a huge time burden on domain experts.
Used in many industrial applications, centrifugal pumps have optimal operating criteria specified at design. These criteria may not be precisely adhered to during operation which will ultimately reduce the life of the asset. Operators would therefore benefit from anticipating how often the design point is deviated from and hence how much asset degradation results. For centrifugal pumps, a novel set of covariates were proposed in this paper which formally partition observed operating zones with an Empirical Bivariate Quantile Partitioned distribution. This captured the dependency relation between operating parameters across plant configurations to predict the component wear that results from particular settings. The effectiveness of this was demonstrated through an operational case study in civil nuclear generation feedwater pumps where corroboration with bearing movements provides an indicator of plant wear. Such a technique is envisaged to inform operators of optimal plant configuration from multiple possibilities in advance of undertaking them.
Nuclear plant operators are required to understand the uncertainties associated with the deployment of prognostics toolsin order to justify their inclusion in operational decision making processes and satisfy regulatory requirements. Operationaluncertainty can cause underlying prognostics models to underperform on assets that are subject to evolving impactsof age, manufacturing tolerances, operating conditions, and operating environment effects, of which may be capturedthrough a condition monitoring (CM) system that itself may be degraded. Sources of uncertainty in the data acquisitionpipeline can impact the health of CM data used to estimate the remaining useful life (RUL) of assets. These uncertaintiescan disguise or misrepresent developing faults, where (for example) the fault identification is not achieved until it hasprogressed to an unmanageable state. This leaves little flexibility for the operator’s maintenance decisions and generallyundermines model confidence. One method to quantify and account for operational uncertainty is calibrated hybrid models, employing physics, knowledgeor data driven methods to improve model accuracy and robustness. Hybrid models allow known physical relations tooffset full reliance on potentially untrustworthy data, whilst reducing the need for an abundance of representative historicaldata to reliably identify the monitored asset’s underlying behavioural trends. Calibration of the model then ensuresthe model is updated and representative of the real monitored asset by accounting for differences between the physics orknowledge model and CM data. In this paper, an open-source bearing knowledge informed machine learning (ML) model and CM datasets are utilizedin an illustrative bearing prognostic application. The uncertainty incurred by the decisions made at key stages in thedevelopment of the model’s data acquisition and processing pipeline are assessed and demonstrated by the resultant impacton RUL prediction performance. It was shown that design decisions could result in multiple valid pipeline designswhich generated different predicted RUL trajectories, increasing the uncertainty in the model output.
Within the field of power generation, aging assets and a desire for improved maintenance decision-making tools have led to growing interest in asset prognostics. Valve failures can account for 7% or more of mechanical failures, and since a conventional power station will contain many hundreds of valves, this represents a significant asset base. This paper presents a prognostic approach for estimating the remaining useful life (RUL) of valves experiencing degradation, utilizing a similarity-based method. Case study data is generated through simulation of valves within a 400MW Combined Cycle Gas Turbine power station. High fidelity industrial simulators are often produced for operator training, to allow personnel to experience fault procedures and take corrective action in a safe, simulation environment, without endangering staff or equipment. This work repurposes such a high fidelity simulator to generate the type of condition monitoring data which would be produced in the presence of a fault. A first principles model of valve degradation was used to generate multiple run-to-failure events, at different degradation rates. The associated parameter data was collected to generate a library of failure cases. This set of cases was partitioned into training and test sets for prognostic modeling and the similarity based prognostic technique applied to calculate RUL. Results are presented of the technique’s accuracy, and conclusions are drawn about the applicability of the technique to this domain.
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