A smart and intuitive machine condition monitoring in the Industry 4.0 scenario

Dinardo, Giuseppe; Fabbiano, Laura; Vacca, Gaetano

doi:10.1016/j.measurement.2018.05.041

Cited by 53 publications

(30 citation statements)

References 18 publications

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“…With improved connectivity and dramatically increased access to low-cost computational power, many industries are currently on the verge of a second digital revolution known as ‘Industry 4.0′ [1]. Of the many advantages that can potentially be leveraged by Industry 4.0, the promise of increased integration of the real-time control, monitoring, and operational aspects of equipment in the process industries is one of the most attractive [1,2]. It has previously been suggested that one of the so-called grand challenges which can be addressed by digitization is the notion of the ‘sustainable, 100% available plant’ [1].…”

Section: Introductionmentioning

confidence: 99%

“…The 100% availability notion captures the vision that in the future there will be only ever be planned maintenance stops; while clearly never achievable in practice, the right balance between maintenance cost and risk must clearly be aimed for. At the heart of this aspect of industrial digitalization the topics of real-time Condition Monitoring (CM) and Predictive Maintenance (PM) can be found [1,2]. As with other Internet-of-Things and Industry 4.0 applications, CM/PM applications can potentially generate large volumes of high-velocity real-time data; therefore, it is beneficial to run local processing of this data at the edge of the network (on an ‘edge device’), to reduce the frequency and real-time requirements of communication to core devices [3].…”

Section: Introductionmentioning

confidence: 99%

“…As with other Internet-of-Things and Industry 4.0 applications, CM/PM applications can potentially generate large volumes of high-velocity real-time data; therefore, it is beneficial to run local processing of this data at the edge of the network (on an ‘edge device’), to reduce the frequency and real-time requirements of communication to core devices [3]. In addition, since it is essential to have adequate fault indices that could serve as indicators in order to predict early failures and create an effective PM schedule [2], in an Industry 4.0 context an appropriate approach would seem to be to distribute the tasks of CM/indices generation and PM analytics between the network edges and Core.…”

Section: Introductionmentioning

confidence: 99%

“…A Modbus RTU server is implemented on the device, and wider connectivity to the network core is provided through an additional Serial to Mobile Data (3G/4G) gateway. The core is able to pull preprocessed and summarized digital data related to local real-time parameter measurements and estimations at a lower velocity through this gateway, where further analytics related to PM are applied; this provides an architecture in keeping with the decentralized, hierarchical structure of the digitalization revolution and IoT [1,2,3]. The focus of this paper is principally upon the implementation and testing of the edge device and soft sensors for CM.…”

Section: Introductionmentioning

confidence: 99%

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An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

Short

Twiddle

2019

Sensors

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This paper is concerned with the implementation and field-testing of an edge device for real-time condition monitoring and fault detection for large-scale rotating equipment in the UK water industry. The edge device implements a local digital twin, processing information from low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters to help detect equipment weasr before damage occurs. The paper describes the implementation of the edge system on a prototype microcontroller-based embedded platform, which supports the Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core, allowing further detailed analytics for predictive maintenance to take place. The paper first describes validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials on large-scale pumping equipment in the field. The paper concludes that the proposed system potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring and predictive maintenance applications in the water industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Industrial Digitalization Platform for Condition Monitoring and Predictive Maintenance of Pumping Equipment

Short

Twiddle

2019

Sensors

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“…The idea is supported by other requirements for Industry 4.0, such as self-organisation, self-adaptation, reconfigurability, self-awareness etc. (Pisching et al, 2018;Dinardo et al, 2018;Wan et al, 2017). They will be part of the process management of an intelligent network of a value chain.…”

Section: Introductionmentioning

confidence: 99%

Performance of an automated process model discovery – the logistics process of a manufacturing company

Halaška

Šperka

2019

Engineering Management in Production and Services

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The simulation and modelling paradigms have significantly shifted in recent years under the influence of the Industry 4.0 concept. There is a requirement for a much higher level of detail and a lower level of abstraction within the simulation of a modelled system that continuously develops. Consequently, higher demands are placed on the construction of automated process models. Such a possibility is provided by automated process discovery techniques. Thus, the paper aims to investigate the performance of automated process discovery techniques within the controlled environment. The presented paper aims to benchmark the automated discovery techniques regarding realistic simulation models within the controlled environment and, more specifically, the logistics process of a manufacturing company. The study is based on a hybrid simulation of logistics in a manufacturing company that implemented the AnyLogic framework. The hybrid simulation is modelled using the BPMN notation using BIMP, the business process modelling software, to acquire data in the form of event logs. Next, five chosen automated process discovery techniques are applied to the event logs, and the results are evaluated. Based on the evaluation of benchmark results received using the chosen discovery algorithms, it is evident that the discovery algorithms have a better overall performance using more extensive event logs both in terms of fitness and precision. Nevertheless, the discovery techniques perform better in the case of smaller data sets, with less complex process models. Typically, automated discovery techniques have to address scalability issues due to the high amount of data present in the logs. However, as demonstrated, the process discovery techniques can also encounter issues of opposite nature. While discovery techniques typically have to address scalability issues due to large datasets, in the case of companies with long delivery cycles, long processing times and parallel production, which is common for the industrial sector, they have to address issues with incompleteness and lack of information in datasets. The management of business companies is becoming essential for companies to stay competitive through efficiency. The issues encountered within the simulation model will be amplified through both vertical and horizontal integration of the supply chain within the Industry 4.0. The impact of vertical integration in the BPMN model and the chosen case identifier is demonstrated. Without the assumption of smart manufacturing, it would be impossible to use a single case identifier throughout the entire simulation. The entire process would have to be divided into several subprocesses.

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