A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case

From a practical point of view, a turbine load cycle (TLC) is defined as the time a turbine in a power plant remains in operation. TLC is used by many electric power plants as a stop indicator for turbine maintenance. In traditional operations, a maximum time for the operation of a turbine is usually estimated and, based on the TLC, the remaining operating time until the equipment is subjected to new maintenance is determined. Today, however, a better process is possible, as there are many turbines with sensors that carry out the telemetry of the operation, and machine learning (ML) models can use this data to support decision making, predicting the optimal time for equipment to stop, from the actual need for maintenance. This is predictive maintenance, and it is widely used in Industry 4.0 contexts. However, knowing which data must be collected by the sensors (the variables), and their impact on the training of an ML algorithm, is a challenge to be explored on a case-by-case basis. In this work, we propose a framework for mapping sensors related to a turbine in a hydroelectric power plant and the selection of variables involved in the load cycle to: (i) investigate whether the data allow identification of the future moment of maintenance, which is done by exploring and comparing four ML algorithms; (ii) discover which are the most important variables (MIV) for each algorithm in predicting the need for maintenance in a given time horizon; (iii) combine the MIV of each algorithm through weighting criteria, identifying the most relevant variables of the studied data set; (iv) develop a methodology to label the data in such a way that the problem of forecasting a future need for maintenance becomes a problem of binary classification (need for maintenance: yes or no) in a time horizon. The resulting framework was applied to a real problem, and the results obtained pointed to rates of maintenance identification with very high accuracies, in the order of 98%

Distribution centers location: contributions to the mathematical modeling.

A localização de instalações está entre as mais importantes decisões logísticas. A questão é tratada, em geral, por técnicas de Pesquisa Operacional, principalmente a programação linear inteira mista, cuja modelagem tem aspectos que podem ser aprimorados. Esta tese apresenta uma proposta metodológica subdividindo o problema em duas fases, visando localizar centros de distribuição de carga (CDs). A Fase 1 define as localizações ótimas dos CDs, iniciando com uma agregação de nós de demanda em clusters através de uma heurística, complementada pela metaheurística simulated annealing (SA). Em seguida, buscam-se, em cada cluster, os melhores locais candidatos. Entre estes candidatos definem-se as localizações ótimas, através de um modelo de programação linear inteira mista ou por SA. Foi conduzido um experimento controlado, com solução ótima conhecida, tendo coincidido em vários casos com a solução obtida através da metodologia proposta. A Fase 2 define a quantidade ótima de CDs, modelando os custos de estoque, armazenagem e vendas perdidas, não considerados na primeira fase. Estes modelos exógenos ao modelo de otimização da primeira fase podem trabalhar sem a restrição de linearidade, trazendo assim, maior realismo a modelagem. Ao final consolida-se o conjunto de custos logísticos (Fases 1 e 2) de forma a se identificar a quantidade de CDs que conduz ao mínimo custo total. A metodologia foi aplicada a um caso real com mais de duzentos pontos de demanda distribuídos sobre os três estados da região sul do país. Os resultados mostraram a aplicabilidade da abordagem proposta.The facility location is one of the most important logistic decisions. The problem is usually handled through the use of operations research techniques, mainly with the use of mixed integer linear programming (MILP), which modeling process can be improved in some of its aspects. This thesis presents a methodological proposal subdividing the problem in two stages, in order to locate distribution centers (DCs). Stage 1 identifies the optimal DC locations, starting with a heuristics, complemented by the metaheuristic simulated annealing (SA), to cluster the demand nodes. Next, the heuristics searches for best DCs candidates in each cluster. Among these best candidates are defined the optimal locations, with the use of a MILP model or through the use of SA. In order to validate the methodology, it was conducted a controlled experiment, with a known optimal solution, having matched in many cases with the solutions obtained through the proposed approach. Stage 2 defines the optimal DC number, modeling the costs of inventory, storage and lost sales, which were not considered in the first stage. These models, exogenous to the Stage 1 optimization model, could represent the costs behavior without the linear restriction, bringing more realism to the modeling process. Finally, the whole set of logistic costs (Stages 1 and 2) is combined in order to identify the DC quantity with minimal total cost. The methodology was applied to a real world problem encompassing more than two hundred demand points spread over the three states of the southern Brazilian region. The results have shown the applicability of the proposed approach

Distribution centers location: contributions to the mathematical modeling.

A localização de instalações está entre as mais importantes decisões logísticas. A questão é tratada, em geral, por técnicas de Pesquisa Operacional, principalmente a programação linear inteira mista, cuja modelagem tem aspectos que podem ser aprimorados. Esta tese apresenta uma proposta metodológica subdividindo o problema em duas fases, visando localizar centros de distribuição de carga (CDs). A Fase 1 define as localizações ótimas dos CDs, iniciando com uma agregação de nós de demanda em clusters através de uma heurística, complementada pela metaheurística simulated annealing (SA). Em seguida, buscam-se, em cada cluster, os melhores locais candidatos. Entre estes candidatos definem-se as localizações ótimas, através de um modelo de programação linear inteira mista ou por SA. Foi conduzido um experimento controlado, com solução ótima conhecida, tendo coincidido em vários casos com a solução obtida através da metodologia proposta. A Fase 2 define a quantidade ótima de CDs, modelando os custos de estoque, armazenagem e vendas perdidas, não considerados na primeira fase. Estes modelos exógenos ao modelo de otimização da primeira fase podem trabalhar sem a restrição de linearidade, trazendo assim, maior realismo a modelagem. Ao final consolida-se o conjunto de custos logísticos (Fases 1 e 2) de forma a se identificar a quantidade de CDs que conduz ao mínimo custo total. A metodologia foi aplicada a um caso real com mais de duzentos pontos de demanda distribuídos sobre os três estados da região sul do país. Os resultados mostraram a aplicabilidade da abordagem proposta.The facility location is one of the most important logistic decisions. The problem is usually handled through the use of operations research techniques, mainly with the use of mixed integer linear programming (MILP), which modeling process can be improved in some of its aspects. This thesis presents a methodological proposal subdividing the problem in two stages, in order to locate distribution centers (DCs). Stage 1 identifies the optimal DC locations, starting with a heuristics, complemented by the metaheuristic simulated annealing (SA), to cluster the demand nodes. Next, the heuristics searches for best DCs candidates in each cluster. Among these best candidates are defined the optimal locations, with the use of a MILP model or through the use of SA. In order to validate the methodology, it was conducted a controlled experiment, with a known optimal solution, having matched in many cases with the solutions obtained through the proposed approach. Stage 2 defines the optimal DC number, modeling the costs of inventory, storage and lost sales, which were not considered in the first stage. These models, exogenous to the Stage 1 optimization model, could represent the costs behavior without the linear restriction, bringing more realism to the modeling process. Finally, the whole set of logistic costs (Stages 1 and 2) is combined in order to identify the DC quantity with minimal total cost. The methodology was applied to a real world problem encompassing more than two hundred demand points spread over the three states of the southern Brazilian region. The results have shown the applicability of the proposed approach

A Framework for Big Data Analytical Process and Mapping—BAProM: Description of an Application in an Industrial Environment

This paper presents an application of a framework for Big Data Analytical Process and Mapping—BAProM—consisting of four modules: Process Mapping, Data Management, Data Analysis, and Predictive Modeling. The framework was conceived as a decision support tool for industrial business, encompassing the whole big data analytical process. The first module incorporates in big data analytical a mapping of processes and variables, which is not common in such processes. This is a proposal that proved to be adequate in the practical application that was developed. Next, an analytical “workbench” was implemented for data management and exploratory analysis (Modules 2 and 3) and, finally, in Module 4, the implementation of artificial intelligence algorithm support predictive processes. The modules are adaptable to different types of industry and problems and can be applied independently. The paper presents a real-world application seeking as final objective the implementation of a predictive maintenance decision support tool in a hydroelectric power plant. The process mapping in the plant identified four subsystems and 100 variables. With the support of the analytical workbench, all variables have been properly analyzed. All underwent a cleaning process and many had to be transformed, before being subjected to exploratory analysis. A predictive model, based on a decision tree (DT), was implemented for predictive maintenance of equipment, identifying critical variables that define the imminence of an equipment failure. This DT model was combined with a time series forecasting model, based on artificial neural networks, to project those critical variables for a future time. The real-world application showed the practical feasibility of the framework, particularly the effectiveness of the analytical workbench, for pre-processing and exploratory analysis, as well as the combined predictive model, proving effectiveness by providing information on future events leading to equipment failures

A Machine Learning Modeling Framework for Predictive Maintenance Based on Equipment Load Cycle: An Application in a Real World Case

From a practical point of view, a turbine load cycle (TLC) is defined as the time a turbine in a power plant remains in operation. TLC is used by many electric power plants as a stop indicator for turbine maintenance. In traditional operations, a maximum time for the operation of a turbine is usually estimated and, based on the TLC, the remaining operating time until the equipment is subjected to new maintenance is determined. Today, however, a better process is possible, as there are many turbines with sensors that carry out the telemetry of the operation, and machine learning (ML) models can use this data to support decision making, predicting the optimal time for equipment to stop, from the actual need for maintenance. This is predictive maintenance, and it is widely used in Industry 4.0 contexts. However, knowing which data must be collected by the sensors (the variables), and their impact on the training of an ML algorithm, is a challenge to be explored on a case-by-case basis. In this work, we propose a framework for mapping sensors related to a turbine in a hydroelectric power plant and the selection of variables involved in the load cycle to: (i) investigate whether the data allow identification of the future moment of maintenance, which is done by exploring and comparing four ML algorithms; (ii) discover which are the most important variables (MIV) for each algorithm in predicting the need for maintenance in a given time horizon; (iii) combine the MIV of each algorithm through weighting criteria, identifying the most relevant variables of the studied data set; (iv) develop a methodology to label the data in such a way that the problem of forecasting a future need for maintenance becomes a problem of binary classification (need for maintenance: yes or no) in a time horizon. The resulting framework was applied to a real problem, and the results obtained pointed to rates of maintenance identification with very high accuracies, in the order of 98%

Prediction of Motor Failure Time Using An Artificial Neural Network

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries