LearningADD: Machine learning based acoustic defect detection in factory automation

Zhang, Tao; Ding, Biyun; Zhao, Xin; Liu, Ganjun; Pang, Zhibo

doi:10.1016/j.jmsy.2021.04.005

Cited by 24 publications

(12 citation statements)

References 33 publications

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“…Data that is produced by different sources in a continuous fashion fits the streams data model according to our review. That is the case of works that process video streams [43,49,56,60], sensors data [14,23,31,36,59,75,83,106,112,126], social media data [22,81,109,123,125], and network metrics [102,116]. Databases schemes are more appropriate as shared data models when system components require to store historical records [9,29,57] or process large amounts of data [105].…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

“…For example, systems can use API calls to collect data from data sources [10,81] or to receive requests from and send responses to end users [49] reviewed papers (i.e., 56.5%) do not adopt data coupling as data is hidden by each systems' components and these components interact using traditional process calls. They can use API calls [14,31,48,55,62,67,75,86,99,102,109] or remote procedure calls (RPC) [8,15,16,29,43,47,57,58,98,101,103,111,123,126] for communication. We found that not all the systems where components share a common data model necessarily follow the data coupling sub-principle.…”

Section: Data As a First Class Citizenmentioning

confidence: 99%

See 1 more Smart Citation

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

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With the upsurge of interest in artificial intelligence machine learning (ML) algorithms, originally developed in academic environments, are now being deployed as parts of real-life systems that deal with large amounts of heterogeneous, dynamic, and high-dimensional data. Deployment of ML methods in real life is prone to challenges across the whole system life-cycle from data management to systems deployment, monitoring, and maintenance. Data-Oriented Architecture (DOA) is an emerging software engineering paradigm that has the potential to mitigate these challenges by proposing a set of principles to create data-driven, loosely coupled, decentralised, and open systems. However DOA as a concept is not widespread yet, and there is no common understanding of how it can be realised in practice. This review addresses that problem by contextualising the principles that underpin the DOA paradigm through the ML system challenges. We explore the extent to which current architectures of ML-based real-world systems have implemented the DOA principles. We also formulate open research challenges and directions for further development of the DOA paradigm.

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Section: Data As a First Class Citizenmentioning

confidence: 99%

Section: Data As a First Class Citizenmentioning

confidence: 99%

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Cabrera¹,

Paleyes²,

Thodoroff³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhang et al [20] designed an acoustic defect detection method using machine learning (ML) techniques. A feature extraction algorithm is utilized for the fault identification procedure that identifies the most relevant characteristics 2 Scientific Programming and patterns.…”

Section: Related Workmentioning

confidence: 99%

Remote Diagnosis and Detection Technology for Electrical Control of Intelligent Manufacturing CNC Machine Tools

Yang

2022

Scientific Programming

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An intelligent manufacturing environment employs internet-based communication and monitoring technologies for fault detection, diagnosis, and monitoring of industrial machines. The monitoring and fault detection are performed remotely without human intervention that predicts faults and ensures specific operational control. This article introduces a rational fault diagnosis process (RFDP) best suited for remote fault detection and diagnosis of CNC machine tools. The proposed process monitors different operational segments of the machine and extracts related data to validate its performance. The interconnection between the segments and fault impact are identified using the transfer learning process. The previously identified faults are used in the state training process to improve detection and diagnosis accuracy. Depending on the operational control continuity, the performance is assessed post the fault diagnosis. The learning paradigm is trained using the machine’s efficiency and rational data processing to predict the transfer states’ faults. The transfer states are modulated based on the efficiency and minimum-maximum control recommended for the CNC machine. This process’s performance is validated using detection accuracy, diagnosis recommendation, downtime, data processing rate, and processing time. From the experimental analysis, it is seen that for the varying data extraction rates, the proposed process improves detection accuracy by 10.14%, diagnosis recommendation by 8.58% and data processing rate by 7.95%, reducing the downtime by 8.85%, and processing by 11.24%.

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“…Teng et al. analyzed the machine tool spindle signal based on the weak feature extraction method of cascaded bistable stochastic resonance system, and developed the condition monitoring system [10–12]. Zhao et al.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Teng et al analyzed the machine tool spindle signal based on the weak feature extraction method of cascaded bistable stochastic resonance system, and developed the condition monitoring system[10][11][12]. Zhao et al designed an optical fibre monitoring system for collecting vibration signals during mechanical operation to monitor the mechanical operation status in real time[13].…”

mentioning

confidence: 99%

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Zhu

Samori

2022

The Journal of Engineering

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The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time-frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time-frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the timefrequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time-frequency characteristic quantity in real time.

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LearningADD: Machine learning based acoustic defect detection in factory automation

Cited by 24 publications

References 33 publications

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Remote Diagnosis and Detection Technology for Electrical Control of Intelligent Manufacturing CNC Machine Tools

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

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