Ensemble clustering for fault diagnosis in industrial plants

International audienceIn this paper, we propose an unsupervised ensemble clustering approach for fault diagnosis in industrial plants. The basic idea is to combine multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final ensemble clustering (P*) is unknown. In practice, a Cluster-based Similarity Partitioning Algorithm (CSPA) is employed to quantify the co-association matrix that describes the similarity among the different base clusterings and, then, a Spectral Clustering technique embedding an unsupervised K-Means algorithm is used to find the optimum number of clusters of P* based on Silhouette validity index calculation. The identified clusters allow distinguishing different operational behaviors of the equipment. The proposed approach is verified with respect to an artificial case study representative of the signal trend behavior of an industrial equipment during shut-down operations. The obtained results have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS): the results show that the novel approach is able to identify the final ensemble clustering with a lower misclassification rate than the CSPA-METIS approach

Invariant methods for an ensemble-based sensitivity analysis of a passive containment cooling system of an AP1000 nuclear power plant

open4noSensitivity Analysis (SA) is performed to gain fundamental insights on a system behavior that is usually reproduced by a model and to identify the most relevant input variables whose variations affect the system model functional response. For the reliability analysis of passive safety systems of Nuclear Power Plants (NPPs), models are Best Estimate (BE) Thermal Hydraulic (TH) codes, that predict the system functional response in normal and accidental conditions and, in this paper, an ensemble of three alternative invariant SA methods is innovatively set up for a SA on the TH code input variables. The ensemble aggregates the input variables raking orders provided by Pearson correlation ratio, Delta method and Beta method. The capability of the ensemble is shown on a BE-TH code of the Passive Containment Cooling System (PCCS) of an Advanced Pressurized water reactor AP1000, during a Loss Of Coolant Accident (LOCA), whose output probability density function (pdf) is approximated by a Finite Mixture Model (FMM), on the basis of a limited number of simulations.Di Maio, Francesco; Nicola, Giancarlo; Borgonovo, Emanuele; Zio, EnricoDI MAIO, Francesco; Nicola, Giancarlo; Borgonovo, Emanuele; Zio, Enric

Valve Health Identification Using Sensors and Machine Learning Methods

Predictive maintenance models attempt to identify developing issues with industrial equipment before they become critical. In this paper, we describe both supervised and unsupervised approaches to predictive maintenance for subsea valves in the oil and gas industry. The supervised approach is appropriate for valves for which a long history of operation along with manual assessments of the state of the valves exists, while the unsupervised approach is suitable to address the cold start problem when new valves, for which we do not have an operational history, come online. For the supervised prediction problem, we attempt to distinguish between healthy and unhealthy valve actuators using sensor data measuring hydraulic pressures and flows during valve opening and closing events. Unlike previous approaches that solely rely on raw sensor data, we derive frequency and time domain features, and experiment with a range of classification algorithms and different feature subsets. The performing models for the supervised approach were discovered to be Adaboost and Random Forest ensembles. In the unsupervised approach, the goal is to detect sudden abrupt changes in valve behaviour by comparing the sensor readings from consecutive opening or closing events. Our novel methodology doing this essentially works by comparing the sequences of sensor readings captured during these events using both raw sensor readings, as well as normalised and first derivative versions of the sequences. We evaluate the effectiveness of a number of well-known time series similarity measures and find that using discrete Frechet distance or dynamic time warping leads to the best results, with the Bray-Curtis similarity measure leading to only marginally poorer change detection but requiring considerably less computational effort

Abrasive effects of sediments on impellers of pumps used for catching raw water.

This study presents an analysis of the abrasive effects of sediments from the bed of the Acre River, Brazil, on the wear of three different ferrous materials employed in the manufacture of impellers of centrifuge pumps used to catch raw water. In order to evaluate the abrasive wear and specific wear coefficient (k) as a function of sediment concentration, tests were conducted in samples of SAE 8620 steel, nodular cast iron and gray cast iron by using a rotary-ball abrasion meter. These tests employed abrasive slurry with concentration of 1, 2, 3, 5 and 10 g L-1 of sediments in distilled water. The volume of worn material as a function of the relative velocity of water flow in relation to the impeller blades was mathematically estimated. The experimental results showed that: i) The semi-angular and semi-rounded shapes of the sediments from the Acre River produced evidence of micro-grooving and plastic deformation in the three metallic alloys; ii) SAE 8620 steel showed higher resistance to abrasive wear than samples of gray and nodular cast iron; iii) the increase in the volume of worn material due to increment in sediment concentration and the relative velocity of the mixture (water + sediment) to the rotor pads.Neste estudo analisou-se a capacidade abrasiva dos sedimentos do leito do Rio Acre, Brasil, no desgaste de 3 materiais ferrosos diferentes utilizados na fabrica??o de rotores de bombas centr?fugas, utilizados na capta??o de ?gua bruta. Para determinar o modo de desgaste e a rela??o do coeficiente de desgaste espec?fico do material (k), em fun??o da concentra??o de sedimentos, foram realizados ensaios em abras?metro de esfera rotativa em amostras de a?o SAE 8620, ferro fundido nodular e em ferro fundido cinzento, usando como suspens?es abrasivas as concentra??es de 1, 2, 3, 5 e 10 g L-1 de sedimento em ?gua destilada. O volume de desgaste em fun??o da velocidade relativa do fluxo da ?gua em rela??o ?s p?s do rotor foi estimado matematicamente. Os resultados mostraram que: i) As formas semiangulares e semiarredondadas dos sedimentos do Rio Acre produziram evidencias de microssulcamento e deforma??o pl?stica nas tr?s ligas met?licas; ii) O a?o SAE 8620 mostrou maior resist?ncia ao desgaste abrasivo do que as amostras de ferros fundidos cinzento e nodular; e iii) O aumento do volume de desgaste decorrente da aumento da concentra??o de sedimento e da velocidade relativa que a mistura (?gua + sedimento) para pelas p?s do rotor

Ensembles of climate change models for risk assessment of nuclear power plants

Climate change affects technical Systems, Structures and Infrastructures (SSIs), changing the environmental context for which SSI were originally designed. In order to prevent any risk growth beyond acceptable levels, the climate change effects must be accounted for into risk assessment models. Climate models can provide future climate data, such as air temperature and pressure. However, the reliability of climate models is a major concern due to the uncertainty in the temperature and pressure future projections. In this work, we consider five climate change models (individually unable to accurately provide historical recorded temperatures and, thus, also future projections), and ensemble their projections for integration in a probabilistic safety assessment, conditional on climate projections. As case study, we consider the Passive Containment Cooling System (PCCS) of two AP1000 Nuclear Power Plants (NPPs). Results provided by the different ensembles are compared. Finally, a risk-based classification approach is performed to identify critical future temperatures, which may lead to PCCS risks beyond acceptable levels

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

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A novel ensemble clustering for operational transients classification with application to a nuclear power plant turbine

International audienceThe objective of the present work is to develop a novel approach for combining in an ensemble multiple base clusterings of operational transients of industrial equipment, when the number of clusters in the final consensus clustering is unknown. A measure of pairwise similarity is used to quantify the co-association matrix that describes the similarity among the different base clusterings. Then, a Spectral Clustering technique of literature, embedding the unsupervised K-Means algorithm, is applied to the co-association matrix for finding the optimum number of clusters of the final consensus clustering, based on Silhouette validity index calculation. The proposed approach is developed with reference to an artificial case study, properly designed to mimic the signal trend behavior of a Nuclear Power Plant (NPP) turbine during shutdown. The results of the artificial case have been compared with those achieved by a state-of-art approach, known as Cluster-based Similarity Partitioning and Serial Graph Partitioning and Fill-reducing Matrix Ordering Algorithms (CSPA-METIS). The comparison shows that the proposed approach is able to identify a final consensus clustering that classifies the transients with better accuracy and robustness compared to the CSPA-METIS approach. The approach is, then, validated on an industrial case concerning 149 shutdown transients of a NPP turbine

Integrating cluster analysis into Multi-Criteria Decision Making for maintenance management of aging culverts

Negligence in relation to aging infrastructure systems could have unintended consequences and is therefore associated with a risk. The assessment of the risk of neglecting maintenance provides valuable information for decision making in maintenance management. However, infrastructure systems are interdependent and interconnected systems of systems characterized by hierarchical levels and a multiplicity of failure scenarios. Assessment methodologies are needed that can capture the multidimensional aspect of risk and simplify the risk assessment, while also improving the understanding and interpretation of the results. This paper proposes to integrate the multi-criteria decision analysis with data mining techniques to perform the risk assessment of aging infrastructures. The analysis is characterized by two phases. First, an intra failure scenario risk assessment is performed. Then, the results are aggregated to carry out an inter failure scenario risk assessment. A cluster analysis based on the k-medoids algorithm is applied to reduce the number of alternatives and identify those which dominate the decision problem. The proposed approach is applied to a system of aging culverts of the German waterways network. Results show that the procedure allows to simplify the analysis and improve communication with infrastructure stakeholders