Energy Consumption Data Based Machine Anomaly Detection

Chen, Hui; Fei, Xiang; Wang, Sheng; Lu, Xin; Jin, Guoqin; Li, Weidong; Wu, Xuyang

doi:10.1109/cbd.2014.24

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…Through this statistic, it can be shown that the researches of big-data based solutions focus on monitoring, prediction, data analytics and propose ICT solutions in manufacturing in Table 3. [57], [56], [125], Regressions [53], ANN [53], PLM [58], Production ERP MP [63], regression [62], K-means [126] MOM/MES [69], [116], [66], regression [127], Distance, Regression, Selforganizing map, principal component analysis [128], SCADA/DCS/ HMI [129], [71], [104], [130], [131], [104], [132] , [112] Classification [133], OPL [106], KM [134], GA [73], O&M [39], [135] [136], [81], [82], [80] logistic regression, naïve Bayes, and a decision tree [109], regression [137], LSM [138], SVM [83], Anomaly detection [139], DTW [140], RF [141], K-means, Markov [142], KD [24],…”

Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

“…This data is mainly used to predict the long-term performance in production planning [62], global manufacturing network design [94], critical event detection in safety [89]. Both historical batching data and real-time streaming data are integrated to train models and monitor real time condition information such as anomaly detection of machines' energy consumption data [139].…”

Section: Driver 2: Datamentioning

confidence: 99%

“…Kafka is a general-purpose messaging system to collect streaming data and publish it to data consumers who subscribe to the topic of data. Kafka is applied in some cases of SCADA [140], O&M [139]. Apache Storm is a real-time data processor, which is used to collect and ingest streaming data to data consumers straightway (O&M [138][139], Quality control [85], PLM [58]).…”

Section: Data Ingestionmentioning

confidence: 99%

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Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

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Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

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Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

Section: Driver 2: Datamentioning

confidence: 99%

Section: Data Ingestionmentioning

confidence: 99%

See 1 more Smart Citation

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

255

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show abstract

“…There is a large body of related research that could be extensively enumerated and discussed [22,23,24,25,26,27]. Most of these approaches show well supported arrangements of bespoke applications integrated to off-the-shelf data management frameworks, standardized file formats and product embedded information devices.…”

Section: Related Workmentioning

confidence: 99%

A cloud-based framework for shop floor big data management and elastic computing analytics

Terrazas

Ferry

Ratchev

2019

Computers in Industry

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Advanced digitalization together with the rise of disruptive Internet technologies are key enablers of a fundamental paradigm shift observed in industrial production. This is known as the fourth industrial revolution (Industry 4.0) which proposes the integration of the new generation of ICT solutions for the monitoring, adaptation, simulation, and optimisation of factories. With the democratization of sensors and actuators, factories and machine tools can now be sensorized and the data generated by these devices can be exploited, for instance, to optimise the utilization of the machines as well as their operation and maintenance. However, analyzing the vast amount of generated data is resource demanding both in terms of computing power and network bandwidth, thus requiring highly scalable solutions. This paper presents a novel big data approach and analytics framework for the management and analysis of machine generated data in the cloud. It brings together standard open source technologies and the exploitation of elastic computing, which, as a whole, can be adapted to and deployed on different cloud computing platforms. This enables reducing infrastructure costs, minimizing deployment difficulty and providing on-demand access to a virtually infinite set of computing power, storage and network resources.

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“…The detection of irregular users through computational intelligence has been treated by many authors with several approaches. The development of intelligent systems has been an alternative and such systems have included techniques as artificial neural networks (ANNs) (Markoč, Hlupić, & Basch, 2011;Zheng, Yang, Niu, Dai, & Zhou, 2018), principle component analysis (PCA) (Singh, Bose, & Joshi, 2017), fuzzy models (Viegas, & Viera, 2017;Nagi et al, 2011), data mining (Chen et al, 2014) and support vector machines (SVMs) (Nagi J. et al, 2010;Pereira et al, 2016).…”

Section: Uparela Gonzalez Jimenez and Quinteromentioning

confidence: 99%

Intelligent system for non-technical losses management in residential users of the electricity sector

Cantillo¹,

González²,

Mares³

et al. 2018

Ing. Inv.

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The identification of irregular users is an important assignment in the recovery of energy in the distribution sector. This analysis requires low error levels to minimize non-technical electrical losses in power grid. However, the detection of fraudulent users who have billing does not present a generalized methodology. This issue is complex and varies according to the case study. This paper presents a novel methodology to identify residential fraudulent users by using intelligent systems. The proposed intelligent system consists of three fundamental modules. The first module performs the classification of users with similar power consumption curves using self-organizing maps and genetic algorithms. The second module allows carrying out the monthly electricity demand forecasting through of recursive adjustment of ARIMA models. The third module performs the detection of fraudulent users through an artificial neural network for pattern recognition. For the design and validation of the proposed intelligent system, several tests were performed in each developed module. The database used for the design and evaluation of the modules was constructed with data supplied by the energy distribution company of the Colombian Caribbean Region. The results obtained by the proposed intelligent system show a better performance versus the detection rates obtained by the company. RESUMENLa identificación de usuarios con consumo fraudulento es una actividad importante en la recuperación de energía en el sector de la distribución. Este análisis requiere bajos niveles de error para minimizar las pérdidas eléctricas no técnicas en la red de distribución. Sin embargo, la detección de usuarios fraudulentos con facturación no tiene una metodología generalizada. Este es un problema complejo y varía de acuerdo con cada caso de estudio. Este artículo presenta una nueva metodología para la identificación inteligente de usuarios fraudulentos residenciales basada en sistemas inteligentes. El sistema inteligente propuesto consiste en tres módulos fundamentales. El primer módulo clasifica a los usuarios con curvas de consumo similares a través de mapas auto-organizativos y algoritmo genéticos. El segundo módulo realiza la predicción de consumos mensuales mediante ajustes recursivos de modelos ARIMA. El tercer módulo es el responsable de llevar a cabo la detección de usuarios irregulares por medio de una red neuronal para reconocimiento de patrones. Para el diseño y validación del sistema inteligente propuesto se realizaron pruebas en cada módulo que lo integra para diferentes tipos de clientes del mercado. La base de datos utilizada para el diseño y evaluación de los módulos fue construida a partir de los datos suministrados por la empresa de distribución de energía de la Costa Caribe Colombiana. Los resultados obtenidos por el sistema inteligente propuesto muestran un mejor desempeño frente a los índices de detección obtenidos por la empresa.Palabras clave: pérdidas no técnicas, consumo irregular de electricidad, detección de fraudes, sistemas i...

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Energy Consumption Data Based Machine Anomaly Detection

Cited by 13 publications

References 22 publications

Manufacturing big data ecosystem: A systematic literature review

Manufacturing big data ecosystem: A systematic literature review

A cloud-based framework for shop floor big data management and elastic computing analytics

Intelligent system for non-technical losses management in residential users of the electricity sector

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