A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

BINet: Multi-perspective Business Process Anomaly Classification

In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets

Detection and Classification of Anomalies in Railway Tracks

Em Portugal, existe uma grande afluência dos transportes ferroviários. Acontece que as empresas que providenciam esses serviços por vezes necessitam de efetuar manutenção às vias-férreas/infraestruturas, o que leva à indisponibilização e/ou atraso dos serviços e máquinas, e consequentemente perdas monetárias. Assim sendo, torna-se necessário preparar um plano de manutenção e prever quando será fundamental efetuar manutenções, de forma a minimizar perdas. Através de um sistema de manutenção preditivo, é possível efetuar a manutenção apenas quando esta é necessária. Este tipo de sistema monitoriza continuamente máquinas e/ou processos, permitindo determinar quando a manutenção deverá existir. Uma das formas de fazer esta análise é treinar algoritmos de machine learning com uma grande quantidade de dados provenientes das máquinas e/ou processos. Nesta dissertação, o objetivo é contribuir para o desenvolvimento de um sistema de manutenção preditivo nas vias-férreas. O contributo específico será detetar e classificar anomalias. Para tal, recorrem-se a técnicas de Machine Learning e Deep Learning, mais concretamente algoritmos não supervisionados e semi-supervisionados, pois o conjunto de dados fornecido possui um número reduzido de anomalias. A escolha dos algoritmos é feita com base naquilo que atualmente é mais utilizado e apresenta melhores resultados. Assim sendo, o primeiro passo da dissertação consistiu em investigar quais as implementações mais comuns para detetar e classificar anomalias em sistemas de manutenção preditivos. Após a investigação, foram treinados os algoritmos que à primeira vista seriam capazes de se adaptar ao cenário apresentado, procurando encontrar os melhores hiperparâmetros para os mesmos. Chegou-se à conclusão, através da comparação da performance, que o mais enquadrado para abordar o problema da identificação das anomalias seria uma rede neuronal artifical Autoencoder. Através dos resultados deste modelo, foi possível definir thresholds para efetuar posteriormente a classificação da anomalia.In Portugal, the railway tracks commonly require maintenance, which leads to a stop/delay of the services, and consequently monetary losses and the non-full use of the equipment. With the use of a Predictive Maintenance System, these problems can be minimized, since these systems continuously monitor the machines and/or processes and determine when maintenance is required. Predictive Maintenance systems can be put together with machine and/or deep learning algorithms since they can be trained with high volumes of historical data and provide diagnosis, detect and classify anomalies, and estimate the lifetime of a machine/process. This dissertation contributes to developing a predictive maintenance system for railway tracks/infrastructure. The main objectives are to detect and classify anomalies in the railway track. To achieve this, unsupervised and semi-supervised algorithms are tested and tuned to determine the one that best adapts to the presented scenario. The algorithms need to be unsupervised and semi-supervised given the few anomalous labels in the dataset

Anomaly detection in laser-guided vehicles' batteries: a case study

Detecting anomalous data within time series is a very relevant task in pattern recognition and machine learning, with many possible applications that range from disease prevention in medicine, e.g., detecting early alterations of the health status before it can clearly be defined as "illness" up to monitoring industrial plants. Regarding this latter application, detecting anomalies in an industrial plant's status firstly prevents serious damages that would require a long interruption of the production process. Secondly, it permits optimal scheduling of maintenance interventions by limiting them to urgent situations. At the same time, they typically follow a fixed prudential schedule according to which components are substituted well before the end of their expected lifetime. This paper describes a case study regarding the monitoring of the status of Laser-guided Vehicles (LGVs) batteries, on which we worked as our contribution to project SUPER (Supercomputing Unified Platform, Emilia Romagna) aimed at establishing and demonstrating a regional High-Performance Computing platform that is going to represent the main Italian supercomputing environment for both computing power and data volume.Comment: This paper contains a report on the research work carried out as a collaboration between the Department of Engineering and Architecture of the University of Parma and Elettric80 spa within project SUPER (Supercomputing Unified Platform Emilia Romagna

A Scalable Predictive Maintenance Model for Detecting Wind Turbine Component Failures Based on SCADA Data

In this work, a novel predictive maintenance system is presented and applied to the main components of wind turbines. The proposed model is based on machine learning and statistical process control tools applied to SCADA (Supervisory Control And Data Acquisition) data of critical components. The test campaign was divided into two stages: a first two years long offline test, and a second one year long real-time test. The offline test used historical faults from six wind farms located in Italy and Romania, corresponding to a total of 150 wind turbines and an overall installed nominal power of 283 MW. The results demonstrate outstanding capabilities of anomaly prediction up to 2 months before device unscheduled downtime. Furthermore, the real-time 12-months test confirms the ability of the proposed system to detect several anomalies, therefore allowing the operators to identify the root causes, and to schedule maintenance actions before reaching a catastrophic stage.Comment: Paper presented at the conference IEEE PES General Meeting 2019, August 4-8 (Atlanta, USA

A Feasibility Study towards the On-Line Quality Assessment of Pesto Sauce Production by NIR and Chemometrics

The food industry needs tools to improve the efficiency of their production processes by minimizing waste, detecting timely potential process issues, as well as reducing the efforts and workforce devoted to laboratory analysis while, at the same time, maintaining high-quality standards of products. This can be achieved by developing on-line monitoring systems and models. The present work presents a feasibility study toward establishing the on-line monitoring of a pesto sauce production process by means of NIR spectroscopy and chemometric tools. The spectra of an intermediate product were acquired on-line and continuously by a NIR probe installed directly on the process line. Principal Component Analysis (PCA) was used both to perform an exploratory data analysis and to build Multivariate Statistical Process Control (MSPC) charts. Moreover, Partial Least Squares (PLS) regression was employed to compute real time prediction models for two different pesto quality parameters, namely, consistency and total lipids content. PCA highlighted some differences related to the origin of basil plants, the main pesto ingredient, such as plant age and supplier. MSPC charts were able to detect production stops/restarts. Finally, it was possible to obtain a rough estimation of the quality of some properties in the early production stage through PLS