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

International audienc

Eicosapentaenoic acid induces DNA demethylation in carcinoma cells through a TET1-dependent mechanism

In cancer cells, global genomic hypomethylation is found together with localized hypermethylation of CpG islands within the promoters and regulatory regions of silenced tumor suppressor genes. Demethylating agents may reverse hypermethylation, thus promoting gene re-expression. Unfortunately, demethylating strategies are not efficient in solid tumor cells. DNA demethylation is mediated by ten-eleven translocation enzymes (TETs). They sequentially convert 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC), which is associated with active transcription; 5-formylcytosine; and finally, 5-carboxylcytosine. Although α-linolenic acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid, the major n-3 polyunsaturated fatty acids, have anti-cancer effects, their action, as DNA-demethylating agents, has never been investigated in solid tumor cells. Here, we report that EPA demethylates DNA in hepatocarcinoma cells. EPA rapidly increases 5hmC on DNA, inducing p21Waf1/Cip1 gene expression, which slows cancer cell-cycle progression. We show that the underlying molecular mechanism involves TET1. EPA simultaneously binds peroxisome proliferator-activated receptor γ (PPARγ) and retinoid X receptor α (RXRα), thus promoting their heterodimer and inducing a PPARγ-TET1 interaction. They generate a TET1-PPARγ-RXRα protein complex, which binds to a hypermethylated CpG island on the p21 gene, where TET1 converts 5mC to 5hmC. In an apparent shuttling motion, PPARγ and RXRα leave the DNA, whereas TET1 associates stably. Overall, EPA directly regulates DNA methylation levels, permitting TET1 to exert its anti-tumoral function.-Ceccarelli, V., Valentini, V., Ronchetti, S., Cannarile, L., Billi, M., Riccardi, C., Ottini, L., Talesa, V. N., Grignani, F., Vecchini, A., Eicosapentaenoic acid induces DNA demethylation in carcinoma cells through a TET1-dependent mechanism

A clustering approach for mining reliability big data for asset management

Big data from very large fleets of assets challenge the asset management, as the number of maintenance strategies to optimize and administrate may become very large. To address this issue, we exploit a clustering approach that identifies a small number of sets of assets with similar reliability behaviors. This enables addressing the maintenance strategy optimization issue once for all the assets belonging to the same cluster and, thus, introduces a strong simplification in the asset management. However, the clustering approach may lead to additional maintenance costs, due to the loss of refinement in the cluster reliability model. For this, we propose a cost model to support asset managers in trading off the simplification brought by the cluster-based approach against the related extra costs. The proposed approach is applied to a real case study concerning a set of more than 30,000 switch point machines

A Novel Method for Sensor Data Validation based on the analysis of Wavelet Transform Scalograms

Sensor data validation has become an important issue in the operation and control of energy production plants. An undetected sensor malfunction may convey inaccurate or misleading information about the actual plant state, possibility leading to unnecessary downtimes and, consequently, large financial losses. The objective of this work is the development of a novel sensor data validation method to promptly detect sensor malfunctions. The proposed method is based on the analysis of data regularity properties, through the joint use of Continuous Wavelet Transform and image analysis techniques. Differently from the typical sensor data validation techniques which detect a sensor malfunction by observing variations in the relationships among measurements provided by different sensors, the proposed method validates the data collected by a given sensor only using historical data collected from the sensor itself. The proposed method is shown able to correctly detect different types and intensities of sensor malfunctions from energy production plants

Handling reliability big data: A similarity-based approach for clustering a large fleet of assets

International audienc

Le tonsilliti croniche ricorrenti

Viene descritto diverso ruolo dei batteri anaerobi beta lattamasi resistenti del core vs surfac

An unsupervised clustering method for assessing the degradation state of cutting tools used in the packaging industry

International audienc

Homogeneous Continuous-Time, Finite-State Hidden Semi-Markov Modeling for Enhancing Empirical Classification System Diagnostics of Industrial Components

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS

The Aramis Data Challenge to prognostics and health management methods for application in evolving environments

International audienceA recurrent difficulty for the effective application of Prognostics and Health Management (PHM) methods is related to the “evolving environments” in which industrial components typically operate. Several factors render the operational environments evolving, including deterioration of components, effects of maintenance activities and changes in working conditions. The issue of evolving environments is even more complicated for multi-component systems, where the degradation of one component can affect the degradation processes of other components, thus modifying their lifetime distributions and the statistical properties of the monitored signals. In an effort to convey research toward practical PHM solutions capable of dealing with evolving environments, the “Aramis challenge on degradation state assessment in evolving environments,” has been launched for the ESREL2020-PSAM15 conference. This work describes the Aramis Data Challenge and associated public dataset, illustrates the methods proposed for its solution and the related results obtained. For the evaluation of the goodness of the fault detection methods, an original metric is introduced, which is a variant of the timeliness metric that has been used in the PHM08 data challenge