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... the first experiment, the raw data was collected from (1) cutting force, (2) vibration, and (3) acoustic emission signal channels. A set of statistical features (28 features) extracted from these signals include Maximum, Median, Mean, and Standard Deviation as listed in Table 4. A predictive model was developed using the random forest algorithm. ...

Citations

... Исследователи в [5] также для получения наиболее полного объема информации о происходящих сбоях в работе оборудования выполняли сбор, подготовку и анализ данных, получаемых из различных источников. Собранные данные авторы [4][5][6][7][8] анализировали с помощью методов статистического анализа, проверяли состав и структуру данных. ...
... Большинство исследователей [4][5][6][7] считают, что извлечение признаков -важный этап предварительной обработки, на котором необработанные данные, собранные из различных сигнальных каналов, преобразуются в набор статистических признаков в формате, поддерживаемом алгоритмами машинного обучения. Так, разработчики нейронной модели [4][5][6][7] определили статистические характеристики, которые затем передали в качестве входных данных для алгоритма машинного обучения. ...
... Большинство исследователей [4][5][6][7] считают, что извлечение признаков -важный этап предварительной обработки, на котором необработанные данные, собранные из различных сигнальных каналов, преобразуются в набор статистических признаков в формате, поддерживаемом алгоритмами машинного обучения. Так, разработчики нейронной модели [4][5][6][7] определили статистические характеристики, которые затем передали в качестве входных данных для алгоритма машинного обучения. Набор статистических характеристик, извлеченных из сигнальных каналов, включал максимум, медиану, среднее значение и стандартное отклонение. ...
Article
One of the features of the mobile data network operators is the need for continuous monitoring and maintenance of equipment and communication channels. The equipment failures that sometimes occur increase the cost of operation and reduce customer loyalty. The ability to predict network malfunctions in advance would be a great solution for mobile operators. The paper discusses the issue of preliminary data preparation of 4G+ mobile network for further use in the development of a neural network model for predicting malfunctions. The results of the analysis of the collected data are presented, the characteristics, composition and data structure that may affect the training of the neural network model later are shown.
... For instance, the works presented in Soualhi et al. (2014) used support vector regression (SVR) to predict the end-of-life of bearings and the end of LEDs lighting time respectively. In the paper (Wu et al., 2016a), the authors used random forest (RF) for predicting the tool wear critical stages while in Wu et al. (2016b) an artificial neural network (ANN) is considered as a regression tool to estimate the attenuation life of charging and discharging of lithium batteries. Besides, considering the same case study, the authors in (Liu et al., 2014) used a combination of ARIMA method with particle filter (PF) as RUL estimators to well represent the degradation trend of the battery capacitors. ...
Article
Data-driven prognostics and health management is an important part of the future industry. It allows the detection of system faults and estimation of its remaining useful life (RUL) to anticipate failures and schedule appropriate prescriptive maintenance actions. Moreover, with the development of data acquisition tools in such industries, it is possible to collect large amount of data from similar systems, which prompts the use of machine learning algorithms for efficient estimation of the system RUL. However, due to different type of faults and degradation rates that can occur in real processes, there exists high variability of the end-of-life (EoL) time of each system degradation trajectory, making more difficult to fix a failure threshold and consequently generate several uncertainties in the estimation of RUL. To address this situation, this paper proposes a new data-driven approach for estimating the system RUL when dealing with the variability of degradation trends and unknown failure thresholds. Particularly, the proposed approach combines two RUL techniques, recursive and direct RUL estimation. First, the historical collected raw data are fed into a processing algorithm to construct prognostic health indicators (HIs) and choose the one that characterizes well the system’s degradation trajectory. This latter indicator allows identifying the high and low EoL amplitude values from the historical data and is used to build a recursive prediction model that estimates in long-term the degradation trend evolution. After that, the trained model forecasts every degradation trend which EoL amplitude is less than the predefined high value. Thus, a set of possible RULs of each trajectory can be predicted. Finally, the ensemble of the derived RULs and their HI trajectories are fused to directly estimate the final RUL. The proposed approach is applied to a subway door system with multiple degradation scenarios while taking into account different operating conditions.
... Li et al. integrated the member algorithms to predict the RUL of aircraft engines and aircraft bearings, and the results showed that the ensemble learning prognostic was less able to predict error [22]. Wu et al. proposed the RF-related algorithms that aggregate the multiple decision trees to forecast the tool wear in the dry milling process [23]. Some researchers used an RF-related algorithm to forecast software defects [24]. ...
Article
Full-text available
With the rapid development of digital transformation, paper forms are digitalized as electronic forms (e-Forms). Existing data can be applied in predictive maintenance (PdM) for the enabling of intelligentization and automation manufacturing. This study aims to enhance the utilization of collected e-Form data though machine learning approaches and cloud computing to predict and provide maintenance actions. The ensemble learning approach (ELA) requires less computation time and has a simple hardware requirement; it is suitable for processing e-form data with specific attributes. This study proposed an improved ELA to predict the defective class of product data from a manufacturing site’s work order form. This study proposed the resource dispatching approach to arrange data with the corresponding emailing resource for automatic notification. This study’s novelty is the integration of cloud computing and an improved ELA for PdM to assist the textile product manufacturing process. The data analytics results show that the improved ensemble learning algorithm has over 98% accuracy and precision for defective product prediction. The validation results of the dispatching approach show that data can be correctly transmitted in a timely manner to the corresponding resource, along with a notification being sent to users.
... The handling of such a huge data is difficult to process by regular CPUs and the processing time may be in hours. The issue can be efficiently treated by use of GPUs operated from clouds which will reduce the processing time by a considerable amount [25]. The techniques used for features extraction and classification are detailed in Sect. 2. Experimentation is described in Sect. ...
Chapter
Full-text available
The traditional methods of measuring surface roughness make use of a stylus-based instrument. Accuracy of this instrument depends upon the roughness in the surface and is an indirect method that involves down time. Machine vision techniques have attracted researchers in the area of machining including analysis of surface quality. Grey level co-occurrence matrix (GLCM) is the most widely used statistical technique for feature extraction of machined surfaces. As surface roughness is a widely accepted measure of quality of the machined component, this work aims at prediction of surface roughness value of milled surface using a regression model. The surface roughness values of milled images were found by mechanical means. These images were then subjected to feature extraction by GLCM and discrete wavelet transform (DWT). Based on the available data of images, a multiple linear regression model was developed to predict the surface roughness value of the machined surface without actual measurement. The proposed model is tested on various milled surface images and it predicts an accuracy of 84.18% for freshly milled surfaces.
... Os dados coletados a partir dos vários sensores em ambientes da indústria fornecem novas oportunidades para soluções de previsão de vida restante de um ativo (YAN et al., 2017). A ideia de que a PdM é capaz de gerar agendamento de ações com base no desempenho ou nas condições do equipamento torna-se primordial para o futuro da indústria (WU et al., 2016). Um dos principais requisitos para a efetiva realização de PdM é a quantidade suficiente de dados de todas as partes do processo de fabricação (KIANGALA; WANG, 2018). ...
... Dos três métodos, é destacado as abordagens baseadas em dados, devido ao aumento na aquisição de dados e à utilização de teorias relacionadas à IA, especialmente as RNAs. Foi analisado cinco trabalhos de um grupo que cita o crescimento da inteligência baseada em dados, com soluções preditivas usando Random Forest (RF) afirmam que em alguns casos, técnicas de aprendizado de máquina não são computacionalmente eficientes para o PHM (WU et al., 2017a;WU et al., 2016;WU et al., 2017d;WU et al., 2017c;WU et al., 2017b)). Outro exemplo são as soluções de detecção de anomalias que exigem volumes de informações históricas, em Wang et al. (2018) é comparada a detecção de anomalias em um e em vários sensores. ...
... Os artigos estão listados na Tabela 5. Neste contexto, é iniciado com um grupo de pesquisas que utilizaram RF, pois devido às suas características, seus modelos colaboram na identificação e visualização de problemas relacionados ao ML, principalmente na detecção de anomalias. Um algoritmo de ML baseado em nuvem foi aplicado RF em paralelo com MapReduce, resultando no que foi denominado como MapReduce-Based Parallel Random Forests (PRFs) (WU et al., 2017a;WU et al., 2016). Toda a estrutura é montada em um sistema de computação em nuvem escalável, e os testes foram realizados usando dados de monitoramento de condições coletados de experimentos em ativos de fresamento. ...
Thesis
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CONTEXT: Industry 4.0 (I4.0) provides connectivity, data volume, new devices, miniaturization, inventory reduction, personalization, and controlled production. In this new era, production customization and data availability are essential to generate information that allows decision-making. The possibility of predicting the need for maintenance in the future and using this information for other processes is one of the manufacturing process challenges. In this context, this thesis proposal transcends the specific fact of applying predictive maintenance (PdM) and suggests ways to integrate processes, focusing on maintenance and production schedules. OBJECTIVE: The objective is to create the Predictive Maintenance & Schedule (PdMS) to integrate maintenance and production schedules in a predictive way. At each sensor data reading and operational information, the machine’s remaining useful life (RUL) is predicted, deciding whether the machine will be part of the production process or not. Reinforcing that, this new Industry scenario allows Computing Applications, together with artificial intelligence and distributed computing, to become more effective in manufacturing processes. With the PdMS creation, the idea is to reduce downtime, improve communication between the maintenance and production sectors and allow future integration with the production, storage, and logistics sectors. METHODOLOGY: The PdMS creation process was divided into two phases: (i) related to PdM, which describes to create and combine degradation indices using similarity patterns and application Savitzky-Golay and Kalman smoothing filters that allow noisy data to identify time-based failures; (ii) related to the scheduling problem and the integration with the results generated by the PdM, which describes the schedule generation, maintenance verification and graphics generation to control and follow up the production schedule. To evaluate the PdMS, a sample predictive maintenance dataset provided by Microsoft was used. We searched for data with characteristics that could contribute to the idea of defining an approach that encourages the adoption of predictive maintenance in factories that already have telemetry in their assets but still perform corrective or preventive maintenance. RESULTS: To evaluate the results, we compared several models based on Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN). Regression Random Forest (RRF) was used to contribute to feature selection and was performed a comparison between Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Networks, and Deep Feed Forward (DFF) network. The results were visually evaluated and by criteria based on errors: Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Squared Error (MSE), Determination Coefficient R2 and Mean Absolute Percentage Error (MAPE). The best results presentes RMSE = 8.789; MSE = 77.253; MAE = 2.262; R2 = 0.848; MAPE = 92.22. CONCLUSION: As a contribution, this work brings a systematic review with a taxonomy proposal, challenges identification, and open questions regarding I4.0 with a focus on PdM. The PdMS model was created from the challenges presented, which presented the decisions, strategies, and architecture that resulted in the prediction of failures in noisy data with five-day anticipation in the data set used for the experiment, thus enabling the intended outcome integration simulation.
... Hence, this method is classified as Training, Recognizing, and Data collection model. Wu [10] introduced a novel cloud-based model with ML technique for machinery prognostics. It makes use of Random Forest (RF) classifier to predict dry mill operations. ...
... Though the PSO-GS and GA-FS models achieved reasonable best costs such as 0.03656 and 0.03440, the OCS-FS model outperformed other FS models with an optimal best cost of 0.00986. It is also displayed that the OCS-FS algorithm selected a set of 12 features out of 24, i.e., 2, 4, 5, 8, 10, 12, 13, 14, 16, 19, 20, and 22. 15,12,24,23,13,20,11,8,18,3,9,1,14,5,2,6,17, 0.03440 16,24,13,9,14,17,22,19,2,15,23,18,12,6,4,10,3,20 PCA 0.04570 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18 CFS 0.79000 4,6,7,10,15,17,19, 22 7 shows the comparative analysis of FPA-DNN method against previous models [19][20][21] in terms of sensitivity and specificity. The figure implies that the Olex-GA approach performed poor in terms of diagnosis and reached a minimum sensitivity of 80% and specificity of 66.66%. ...
... Though the PSO-GS and GA-FS models achieved reasonable best costs such as 0.03656 and 0.03440, the OCS-FS model outperformed other FS models with an optimal best cost of 0.00986. It is also displayed that the OCS-FS algorithm selected a set of 12 features out of 24, i.e., 2, 4, 5, 8, 10, 12, 13, 14, 16, 19, 20, and 22. 15,12,24,23,13,20,11,8,18,3,9,1,14,5,2,6,17, 0.03440 16,24,13,9,14,17,22,19,2,15,23,18,12,6,4,10,3,20 PCA 0.04570 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18 CFS 0.79000 4,6,7,10,15,17,19, 22 7 shows the comparative analysis of FPA-DNN method against previous models [19][20][21] in terms of sensitivity and specificity. The figure implies that the Olex-GA approach performed poor in terms of diagnosis and reached a minimum sensitivity of 80% and specificity of 66.66%. ...
Article
In recent times, Internet of Things (IoT) and Cloud Computing (CC) paradigms are commonly employed in different healthcare applications. IoT gadgets generate huge volumes of patient data in healthcare domain, which can be examined on cloud over the available storage and computation resources in mobile gadgets. Chronic Kidney Disease (CKD) is one of the deadliest diseases that has high mortality rate across the globe. The current research work presents a novel IoT and cloud-based CKD diagnosis model called Flower Pollination Algorithm (FPA)-based Deep Neural Network (DNN) model abbreviated as FPA-DNN. The steps involved in the presented FPA-DNN model are data collection, preprocessing, Feature Selection (FS), and classification. Primarily, the IoT gadgets are utilized in the collection of a patient's health information. The proposed FPA-DNN model deploys Oppositional Crow Search (OCS) algorithm for FS, which selects the optimal subset of features from the preprocessed data. The application of FPA helps in tuning the DNN parameters for better classification performance. The simulation analysis of the proposed FPA-DNN model was performed against the benchmark CKD dataset. The results were examined under different aspects. The simulation outcomes established the superior performance of FPA-DNN technique by achieving the highest sensitivity of 98.80%, specificity of 98.66%, accuracy of 98.75%, F-score of 99%, and kappa of 97.33%.
... Model based approaches, data-driven approaches and hybrid prognostic approaches Si et al. 15 2011 Statistical data driven approaches Kan et al. [11] 2015 ...
Chapter
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Thanks to the applications of advanced technologies like cloud computing, the Internet of Things, Big data analytics, and Artificial Intelligence, we are now transferring to smart manufacturing in which the process of manufacturing becomes more intelligent. The system of machines in production lines requires higher accuracy in operation. Any interruption due to failures or malfunction of machinery can result in significant losses for businesses. One of the important methods to keep the machine system safe and reliable is predictive maintenance. This chapter presents a study on machine learning-based methods for predictive maintenance (PdM). To be more specific, we suggest designing a decision support system using a long short-term memory network with Bayesian optimization to estimate the remaining useful life of the machines, an important method in PdM. We provide a case study to show the performance of our proposed method. Several studies related to the problem of PdM and perspectives for further researches are also reviewed and discussed.
... (2) Knowledge based: is a qualitative method using expert knowledge instead of precise systematic quantitative mathematical models and thereby can reduce the complexity of physical models. It is currently widely used in the field of hybrid strategies, such as expert systems or fuzzy logic (Ayad et al., 2018;Wu et al., 2016). The biggest advantage of expert systems is that they can make full use of the domain expert experience and knowledge related to the target system. ...
Book
The paradigm shift from mass production to on-demand, personalized, customer-driven, and knowledge-based production reshapes manufacturing. Smart manufacturing leads to an automated world that relies more on information and communication technologies (ICTs) and sophisticated information-technology-intensive processes, enhancing flexibility. Furthermore, as automation and digital supply chain management become the norm across enterprise systems, advanced manufacturing becomes increasingly linked at a global level. Manufacturing companies are under pressure to achieve the goals of high competitiveness and profitability in a globalized and volatile environment. To address these challenges, engineers have to develop and implement new design and operation methodologies for production networks taking also into account mass personalization and market uncertainty. In the era of digitalization, the integration of cloud-based approaches can elevate enterprise performance. Therefore, to meet these challenges, new technologies such as cyber- physical systems (CPS), artificial intelligence (AI), augmented reality (AR), big data analytics, the Internet of things (IoT), and the industrial Internet of things (IIoT) must be integrated. The book consists of 12 chapters, written by leading researchers in the field of manufacturing. Chapter 1 presents the peculiarities of the integration of key Industry 4.0 technologies toward the design, planning, and operation of global production networks and the integration of the customer to the design phase of the products, services, and systems. In Chapter 2 recent and future trends of how emerging technologies support the transformations in reconfigurable supply chains and production systems are presented. Chapter 3 is devoted to present the implications for the design and management of global production networks (GPNs) induced by the mass production paradigm (MPP). Chapter 4 aims to identify and highlight the implications in the design and planning of manufacturing networks in the mass personalization environment and Chapter 5 presents the state of the art on adaptive scheduling and developments in smart scheduling within Industry 4.0 paradigm. Chapter 6 presents how modern digital manufacturing technologies may be utilized for reducing and eventually taming the complexity in production systems and networks. Chapter 7 provides an overview of innovative smart scheduling and predictive maintenance (PdM) techniques under smart manufacturing production environments. Chapter 8 reviews the landscape of the industrial Internet of things (IIoT). Chapter 9 presents a generic framework for industrial big data utilization in industrial environments and big data application areas and Chapter 10 aims to map major architectures and applications of digital twins for Industry 4.0. Chapter 11 reviews and presents machine learning (ML) technologies and artificial intelligence (AI) in manufacturing systems. Finally, Chapter 12 demonstrates the real-world applicability of blockchain potential using industrial case studies.
... (2) Knowledge based: is a qualitative method using expert knowledge instead of precise systematic quantitative mathematical models and thereby can reduce the complexity of physical models. It is currently widely used in the field of hybrid strategies, such as expert systems or fuzzy logic (Ayad et al., 2018;Wu et al., 2016). The biggest advantage of expert systems is that they can make full use of the domain expert experience and knowledge related to the target system. ...
Chapter
The industrial Internet of things (IIoT) key technologies advance flexibility, personalization, and cost savings in industrial processes. Smart manufacturing and Industry 4.0 integrate the physical and decision-making aspects of manufacturing processes into autonomous and decentralized systems. Cloud manufacturing is emerging with scheduling issues between process design dynamics, and machine setup. Furthermore, as information and communication technologies (ICTs) have integrated into the cyber-physical systems (CPSs), adaptive scheduling and rescheduling have turned into the cornerstones of smart manufacturing. The cyber-physical production systems (CPPs) link the information technology (IT) systems to establish communication networks. This chapter addresses the state of the art of the ICTs that are the drivers of data-driven innovations and presents the industrial applications that bridge the gap among the industry and academia creating a bifold knowledge and experience network. Finally, the shift toward digitalization intensifies the need for digital skills. Lastly, the problems and future research directions toward the new generation of the production staff are discussed.
... There are different maintenance strategies aimed at enhancing the prediction accuracy of RUL. The PdM approaches organized into the following four levels according to their basic methods, i.e., knowledge-based methods [65,66], physics model-based approaches [67][68][69], data-driven (statistics-based, pattern recognition, or artificial intelligence (AI) and models based on machine learning algorithms) [70][71][72][73], and hybrid approaches. RUL is the time remaining for a component/part to fulfil its operative capabilities before a crash. ...
Article
Full-text available
The feasibility of reliably generating bioenergy from forest biomass waste is intimately linked to supply chain and production processing costs. These costs are, at least in part, directly related to assumptions about the reliability and cost-efficiency of the machinery used along the forestry bioenergy supply chain. Although mechanization in forestry operations has advanced in the last 20 years, it is evident that challenges remain in relation to production capability, standardization of wood quality, and supply guarantee from forestry resources because of the age and reliability of the machinery. An important component in sustainable bioenergy from biomass supply chains will be confidence in consistent production costs linked to guarantees about harvest and haulage machinery reliability. In this context, this paper examines the issue of machinery maintenance and advances in machine learning and big data analysis that are contributing to improved intelligent prediction that is aiding supply chain reliability in bioenergy from woody biomass. The concept of “Industry 4.0” refers to the integration of numerous technologies and business processes that are transforming many aspects of conventional industries. In the realm of machinery maintenance, the dramatic increase in the capacity to dynamically collect, collate, and analyze data inputs including maintenance archive data, sensor-based monitoring, and external environmental and contextual variables. Big data analytics offers the potential to enhance the identification and prediction of maintenance (PdM) requirements. Given that estimates of costs associated with machinery maintenance vary between 20% and 60% of the overall costs, the need to find ways to better mitigate these costs is important. While PdM has been shown to help, it is noticeable that to-date there has been limited assessment of the impacts of external factors such as weather condition, operator experiences and/or operator fatigue on maintenance costs, and in turn the accuracy of maintenance predictions. While some researchers argue these data are captured by sensors on machinery components, this remains to be proven and efforts to enhance weighted calibrations for these external factors may further contribute to improving the prediction accuracy of remaining useful life (RUL) of machinery. This paper reviews and analyzes underlying assumptions embedded in different types of data used in maintenance regimes and assesses their quality and their current utility for predictive maintenance in forestry. The paper also describes an approach to building ‘intelligent’ predictive maintenance for forestry by incorporating external variables data into the computational maintenance model. Based on these insights, the paper presents a model for an intelligent predictive maintenance system (IPdM) for forestry and a method for its implementation and evaluation in the field.