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-

Data-driven approaches to maintenance policy definition: general framework and applications

La competitività dell’attuale scenario industriale richiede elevati livelli di affidabilità di processo, in particolare per impianti complessi: infatti un elevato numero di componenti li rende potenzialmente più soggetti a guasti. In questo contesto, la tesi mira a proporre un framework generale per supportare il processo di gestione della manutenzione. Si presentano quattro applicazioni basate sul caso di studio di una raffineria di petrolio. Nella prima applicazione, si adotta il framework per derivare le regole di associazione tra i guasti dei componenti dopo un arresto dell'impianto di raffineria. Si identificano i componenti con maggiore probabilità di rottura entro un dato intervallo di tempo dall’arresto dell'impianto e si propone la strategia di manutenzione. La seconda applicazione si basa su un modello di ottimizzazione. Sfruttando le regole di associazione, si formula un modello di programmazione lineare intera per selezionare l'insieme ottimale di componenti da riparare per migliorare l'affidabilità dell'impianto. Nella terza applicazione, si modella un problema bi-obiettivo di riparazione dei componenti per ridurre l'impatto sia sul tempo di recupero da un arresto che sui costi complessivi di manutenzione. Questo è risolto sia attraverso l'approccio AUGMEnted ε-CONstraint sia tramite una meta-euristica Large Neighborhood Search. Nella quarta applicazione, si adottano l'Association Rule Mining (ARM) e la Social Network Analysis (SNA) per identificare le interazioni nascoste tra i componenti che portano ad un effetto domino tra i guasti. Seguendo il framework generale proposto, ARM e SNA vengono applicate anche per perseguire un secondo obiettivo: estendere l'analisi dei processi produttivi analizzando i risultati della Failure Modes Effects and Criticalities Analysis. Si considerano il caso studio di un impianto offshore e onshore per l'estrazione e lo stoccaggio di petrolio e quello di una centrale idroelettrica.The competitiveness characterizing the current industrial scenario requires high levels of process reliability. This aspect is particularly relevant for complex plants since many components are potentially more subject to failure occurrence. In this context, this thesis aims to propose a general framework to support the maintenance management. Four different applications are presented, based on an oil refinery case study. In the first application, the Association Rules describing components failing after a stoppage of the oil refinery plant are mined. The components that are most likely to break within a given time interval after a plant stoppage are identified to propose the best maintenance strategy. The second application regards a predictive optimization-based maintenance policy, considering the Association Rules. An integer linear programming model is formulated to select the optimal set of components to repair to improve the plant's reliability. In the third application, a bi-objective Component Repairing Problem is developed in order to reduce the impact on both the time to recover from a stoppage and the overall maintenance costs. It is solved through the AUGMEnted ε-CONstraint approach and through a bi-objective Large Neighborhood Search meta-heuristic. In the fourth application, the Association Rule Mining (ARM) and Social Network Analysis (SNA) are contextually adopted to identify the hidden interactions between components that lead to a domino effect between failures. Following the proposed general framework, ARM and SNA are also applied to pursue a second objective: extending the analysis of the production processes in terms of failures and related effects, analyzing the results of the Failure Modes Effects and Criticalities Analysis. An offshore and onshore plant for oil and gas extraction and storing and a hydro-electrical power plant are considered as case studies

Lean principles for organizing items in an automated storage and retrieval system:an association rule mining – based approach

The application of the 5S methodology to warehouse management represents an important step for all manufacturing companies, especially for managing products that consist of a large number of components. Moreover, from a lean production point of view, inventory management requires a reduction in inventory wastes in terms of costs, quantities and time of non-added value tasks. Moving towards an Industry 4.0 environment, a deeper understanding of data provided by production processes and supply chain operations is needed: the application of Data Mining techniques can provide valuable support in such an objective. In this context, a procedure aiming at reducing the number and the duration of picking processes in an Automated Storage and Retrieval System. Association Rule Mining is applied for reducing time wasted during the storage and retrieval activities of components and finished products, pursuing the space and material management philosophy expressed by the 5S methodology. The first step of the proposed procedure requires the evaluation of the picking frequency for each component. Historical data are analyzed to extract the association rules describing the sets of components frequently belonging to the same order. Then, the allocation of items in the Automated Storage and Retrieval System is performed considering (a) the association degree, i.e., the confidence of the rule, between the components under analysis and (b) the spatial availability. The main contribution of this work is the development of a versatile procedure for eliminating time waste in the picking processes from an AS/RS. A real-life example of a manufacturing company is also presented to explain the proposed procedure, as well as further research development worthy of investigation

Implementation of Industry 4.0 Techniques in Lean Production Technology: A Literature Review

Lean thinking and Industry 4.0 have been broadly investigated in recent years in intelligent manufacturing. Lean Production is still one of the most efficient industrial solutions in business and research, despite being implemented for a long time. On the other hand, Industry 4.0 has been introduced referring to the fourth industrial revolution. This study aims to analyze the combination of both Industry 4.0 and Lean production practices through a systematic literature review from a Lean Automation perspective. In this field, 189 articles are examined using VOSviewer for cluster analysis. Then, a more detailed analysis is provided to explore how Industry 4.0 and Lean techniques are integrated from a practical perspective. Results highlighted Big Data Analysis and Value Stream Mapping as the most common techniques, also emphasizing a growing trend toward new publications. Nevertheless, few practical applications are identified in the literature highlighting six gaps in the correlation of LA practices

Defining a data-driven maintenance policy: an application to an oil refinery plant

Purpose – The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements. Design/methodology/approach – The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns. Findings – The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested. Originality/value – This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems

Data Mining and Augmented Reality: An Application to the Fashion Industry

The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed up operations. This is strongly perceived in the quality field, where effective management of the trade-off between increasing quality controls to intercept product defects and decreasing them to reduce the delivery time represents a competitive challenge. A framework to improve data analysis and visualization in quality management is proposed, and its applicability is demonstrated with a case study in the fashion industry. A questionnaire assesses its on-field usability. The main findings refer to overcoming the lack in the literature of a decision support framework based on the joint application of association rules mining and augmented reality. The successful implementation in a real scenario has a twofold aim: on the one hand, sample sizes are strategically revised according to the supplier performance per product category and material; on the other hand, the daily quality controls are speeded up through accurate suggestions about the most occurrent defect and location per product characteristics, integrated with extra tips only for trainees

Big data-driven framework for viral churn prevention: a case study

The application of churn prevention represents an important step for mobile communication companies aiming at increasing customer loyalty. In a machine learning perspective, Customer Value Management departments require automated methods and processes to create marketing campaigns able to identify the most appropriate churn prevention approach. Moving towards a big data-driven environment, a deeper understanding of data provided by churn processes and client operations is needed. In this context, a procedure aiming at reducing the number of churners by planning a customized marketing campaign is deployed through a data-driven approach. Decision Tree methodology is applied to drow up a list of clients with churn propensity: in this way, customer analysis is detailed, as well as the development of a marketing campaign, integrating the individual churn model with viral churn perspective. The first step of the proposed procedure requires the evaluation of churn probability for each customer, based on the influence of his social links. Then, the customer profiling is performed considering (a) individual variables, (b) variables describing customer-company interactions, (c) external variables. The main contribution of this work is the development of a versatile procedure for viral churn prevention, applying Decision Tree techniques in the telecommunication sector, and integrating a direct campaign from the Customer Value Management marketing department to each customer with significant churn risk. A case study of a mobile communication company is also presented to explain the proposed procedure, as well as to analyze its real performance and results

Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem

In systems with many components that are required to be constantly active, such as refineries, predicting the components that will break in a time interval after a stoppage may significantly increase their reliability. However, predicting the set of components to be repaired is a challenging task, especially when several conditions (e.g. breakage probability, repair time and cost) have to be considered simultaneously. A data-driven predictive maintenance policy is proposed for maximizing the system reliability and minimizing the maximum repair time, considering both budget and human resources constraints. Therefore, a data-driven algorithm is designed for extracting component breakage probabilities. Then, two bi-objective optimization approaches are proposed for determining the set of components to repair. The former is based on the formulation of a bi-objective mixed integer linear programming model solved through the AUGMEnted ε-CONstraint (AUGMECON) method. The latter implements a bi-objective large neighbourhood search, outperforming the first approach