Detecting anomalous energy consumptions in distributed manufacturing systems

Faltinski, Sebastian; Flatt, Holger; Pethig, Florian; Kroll, Bjorn; Vodenčarević, Asmir; Maier, Alexander; Niggemann, Oliver

doi:10.1109/indin.2012.6301142

Cited by 31 publications

(18 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This evaluation platform is described in detail in [4] and [21]. The LMF represents a plant for storing, transporting, processing and packing raw material (corn).…”

Section: Discussionmentioning

confidence: 99%

“…changes of these two signals, lead to mode transitions. Note, that the evaluations in [4] and [21] were conducted with a more elaborated algorithm for the estimation of the underlying finite state machine (the Hy-BUTLA algorithm described in [3]) which resulted in a higher number of system modes. The algorithm described in section IV was applied for the learning of the sequence of sub-mode models assigned to the particular modes of the automaton.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

Windmann

Jiao

Niggemann

et al. 2013

2013 11th IEEE International Conference on Industrial Informatics (INDIN)

Self Cite

View full text Add to dashboard Cite

In the presented work, the detection of anomalous energy consumption in hybrid industrial production systems is investigated. A model-based approach with a timed hybrid automaton as overall system model is employed for anomaly detection. The approach is based on the assumption of several system modes, i.e. phases with continuous system behavior. Transitions between the modes are attributed to discrete control events such as on/off signals. The underlying discrete event system which comprises both system modes and transitions is modeled as finite state machine. The focus of this paper is set on the modeling of the energy consumption in the particular system modes. Sequences of stochastic state space models are employed for this purpose. Model learning and anomaly detection for this approach are considered. The proposed approach is further evaluated in a small model factory. The experimental results show significant improvements compared to existing approaches to anomaly detection in hybrid industrial systems

show abstract

“…This evaluation platform is described in detail in [4] and [21]. The LMF represents a plant for storing, transporting, processing and packing raw material (corn).…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

Windmann

Jiao

Niggemann

et al. 2013

2013 11th IEEE International Conference on Industrial Informatics (INDIN)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various services which represent the mechatronic functions of the equipment were defined in the address space of the OPC UA server, thus the flexibility of the manufacturing cells can be enhanced. OPC UA has also been extensively implemented as the communication protocol in power consumption monitoring and energy efficiency analysis systems [38][39][40]. Design and development of an OPC UA information model need to follow a model-driven approach.…”

Section: Opc Ua Related Researchmentioning

confidence: 99%

A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect

Liu

Vengayil

Lu³

et al. 2019

Journal of Manufacturing Systems

186

View full text Add to dashboard Cite

Cyber-Physical Machine Tools (CPMT) represent a new generation of machine tools that are smarter, well connected, widely accessible, more adaptive and more autonomous. Development of CPMT requires standardized information modelling method and communication protocols for machine tools. This paper proposes a CPMT Platform based on OPC UA and MTConnect that enables standardized, interoperable and efficient data communication among machine tools and various types of software applications. First, a development method for OPC UA-based CPMT is proposed based on a generic OPC UA information model for CNC machine tools. Second, to address the issue of interoperability between OPC UA and MTConnect, an MTConnect to OPC UA interface is developed to transform MTConnect information model and data to their OPC UA counterparts. An OPC UA-based CPMT prototype is developed and further integrated with a previously developed MTConnect-based CPMT to establish a CPMT Platform. Third, different applications are developed to demonstrate the advantages of the proposed CPMT Platform, including an OPC UA Client, an advanced AR-assisted wearable Human-Machine Interface and a conceptual framework for CPMT powered cloud manufacturing environment. Experimental results have proven that the proposed CPMT Platform can significantly improve the overall production efficiency and effectiveness in the shop floor.

show abstract

“…Both [18] and [19] developed model-based approaches for detecting anomalous energy consumptions in distributed manufacturing systems. Models were learned and the anomalies were detected by the comparison of the predicted and the observed behaviour.…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Data Based Machine Anomaly Detection

Chen¹,

Fei²,

Wang³

et al. 2014

2014 Second International Conference on Advanced Cloud and Big Data

View full text Add to dashboard Cite

Abstract-The ever increasing of product development and the scarcity of the energy resources that those manufacturing activities heavily rely on have made it of great significance the study on how to improve the energy efficiency in manufacturing environment. Energy consumption sensing and collection enables the development of effective solutions to higher energy efficiency. Further, it is found that the data on energy consumption of manufacturing machines also contains the information on the conditions of these machines. In this paper, methods of machine anomaly detection based on energy consumption information are developed and applied to cases on our Syil X4 computer numerical control (CNC) milling machine. Further, given massive amount of energy consumption data from large amount machining tasks, the proposed algorithms are being implemented on a Storm and Hadoop based framework aiming at online realtime machine anomaly detection.

show abstract

Detecting anomalous energy consumptions in distributed manufacturing systems

Cited by 31 publications

References 11 publications

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems

A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect

Energy Consumption Data Based Machine Anomaly Detection

Contact Info

Product

Resources

About