Tracking Operation Status of Machines through Vibration Analysis using Motif Discovery.

Lee, Yon-Qing; Beh, Woan Lin; Ooi, Boon-Yaik

doi:10.1088/1742-6596/1529/5/052005

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Industrial robot [82] Color sensor Recognise/detect the colour of a material or passing part. TSC230 RGB FMS system [74] Accelerometer Measure the vibration, or acceleration of the machine structure General use accelerometers without specific type Industrial equipment [86], Textile [71], Metal cutting [87] Bosch BMX160…”

Section: Mspa13 Npn Transistormentioning

confidence: 99%

“…Plastic injection [114], Electronic manufacturing [76], Iron & Steel production [84], Limestone processing [90], Industrial equipment [86], Steel Mill [20], Car assembly [75], Oilfield [81], CNC machining [115], [127], [128], Metal cutting [93], Industrial robot [82], Material handling [104], Assembly line [77],…”

Section: Process Parameters Trackingmentioning

confidence: 99%

“…[72]. Besides, due to the loss of productivity during the implementation period, production interruption can be considered as a cost [86]. Some legacy PLCs are irreplaceable due to the risk of production downtime, thus can obstruct the retrofitting effort [57].…”

Section: Fourth Phase: Derive Implementation Roadmapmentioning

confidence: 99%

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Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Tran

Ruppert

Eigner³

et al. 2022

IEEE Access

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The ongoing Industry 4.0 is characterized by the connectivity between components in the manufacturing system. For modern machines, the Internet of Things is a built-in function. In contrast, there are legacy machines in deployment functioning without digital communication. The need to connect them became popular to improve overall production efficiency. As building a new smart factory as a greenfield investment is a capital-intensive choice, retrofitting the existing infrastructure with IoT capability is more reasonable than replacing them. However, this so-called brownfield development, or retrofitting, requires specific prerequisites, e.g., digitization status assessment, technical and connectivity development, management requirement, and operational need, representing a significant disadvantage: lack of scalability. In the meantime, Industry 5.0 is under human-centric priority, which poses new challenges to the retrofitted system. Aware of the challenge, this paper provides a systematic overview of brownfield development regarding technical difficulties, supporting technologies, and possible applications for the legacy system. The research scope focuses on available Industry 4.0 advancements but considers preparing for the forthcoming Industry 5.0. The proposed retrofitting project approach can be a guideline for manufacturers to transform their factories into intelligent spaces with minimal cost and effort but still gain the most applicable solution for management needs. The future direction for other research in brownfield development for Industry 5.0 is also discussed.

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Section: Mspa13 Npn Transistormentioning

confidence: 99%

Section: Process Parameters Trackingmentioning

confidence: 99%

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Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Tran

Ruppert

Eigner³

et al. 2022

IEEE Access

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show abstract

“…Usually, the MP technique is applied to vibration data gathered from gas turbines [20][21][22][23]. In our proposed approach, we used only the outlet energy signal for the anomaly detection, aiming to simplify the interpretation of the possible discords, i.e., a possible anomalous subsequence or pattern, by the end-user.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Ghazvini,

Sànchez-Marrè,

Naderi

et al. 2024

Energies

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Gas turbines play a key role in generating power. It is really important that they work efficiently, safely, and reliably. However, their performance can be adversely affected by factors such as component wear, vibrations, and temperature fluctuations, often leading to abnormal patterns indicative of potential failures. As a result, anomaly detection has become an area of active research. Matrix Profile (MP) methods have emerged as a promising solution for identifying significant deviations in time series data from normal operational patterns. While most existing MP methods focus on vibration analysis of gas turbines, this paper introduces a novel approach using the outlet power signal. This modified approach, termed Cluster-based Matrix Profile (CMP) analysis, facilitates the identification of abnormal patterns and subsequent anomaly detection within the gas turbine engine system. Significantly, CMP analysis not only accelerates processing speed, but also provides user-friendly support information for operators. The experimental results on real-world gas turbines demonstrate the effectiveness of our approach in the early detection of anomalies and potential system failures.

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Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

Zymbler

Иванова

2021

Mathematics

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Currently, big sensor data arise in a wide spectrum of Industry 4.0, Internet of Things, and Smart City applications. In such subject domains, sensors tend to have a high frequency and produce massive time series in a relatively short time interval. The data collected from the sensors are subject to mining in order to make strategic decisions. In the article, we consider the problem of choosing a Time Series Database Management System (TSDBMS) to provide efficient storing and mining of big sensor data. We overview InfluxDB, OpenTSDB, and TimescaleDB, which are among the most popular state-of-the-art TSDBMSs, and represent different categories of such systems, namely native, add-ons over NoSQL systems, and add-ons over relational DBMSs (RDBMSs), respectively. Our overview shows that, at present, TSDBMSs offer a modest built-in toolset to mine big sensor data. This leads to the use of third-party mining systems and unwanted overhead costs due to exporting data outside a TSDBMS, data conversion, and so on. We propose an approach to managing and mining sensor data inside RDBMSs that exploits the Matrix Profile concept. A Matrix Profile is a data structure that annotates a time series through the index of and the distance to the nearest neighbor of each subsequence of the time series and serves as a basis to discover motifs, anomalies, and other time-series data mining primitives. This approach is implemented as a PostgreSQL extension that allows an application programmer both to compute matrix profiles and mining primitives and to represent them as relational tables. Experimental case studies show that our approach surpasses the above-mentioned out-of-TSDBMS competitors in terms of performance since it assumes that sensor data are mined inside a TSDBMS at no significant overhead costs.

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Tracking Operation Status of Machines through Vibration Analysis using Motif Discovery.

Cited by 3 publications

References 17 publications

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Retrofitting-Based Development of Brownfield Industry 4.0 and Industry 5.0 Solutions

Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile

Matrix Profile-Based Approach to Industrial Sensor Data Analysis Inside RDBMS

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