Event Classification from Sensor Data using Spectral Analysis in Robotic Finishing Processes

Pappachan, Bobby Kaniyamkudy; Tjahjowidodo, Tegoeh; Wijaya, Tomi

doi:10.5220/0006204900800086

Cited by 3 publications

(3 citation statements)

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“…A feature from the combined wavelet decomposition and Welch's spectrum estimation is calculated and applied to obtain the raw vibration signal. This feature is selected based on a previous study [12]. The feature extraction results of each pass and the associated chamfer length for each pass are presented in Tables 2 and 3.…”

Section: Research Motivation Experimental Setup and Data Acquisitionmentioning

confidence: 99%

“…Firstly, it is a generic library that allows for the simple integration with major web frameworks. Additionally, it is based on a scalable vector graphics technology and works with HTML5, which is the current trend in data visualization [12]. The HighCharts library is able to create dynamic charts which allow users to interact with the data.…”

Section: Webpage Developmentmentioning

confidence: 99%

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An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0

et al. 2018

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The number of studies on the Internet of Things (IoT) has grown significantly in the past decade and has been applied in various fields. The IoT term sounds like it is specifically for computer science but it has actually been widely applied in the engineering field, especially in industrial applications, e.g., manufacturing processes. The number of published papers in the IoT has also increased significantly, addressing various applications. A particular application of the IoT in these industries has brought in a new term, the so-called Industrial IoT (IIoT). This paper concisely reviews the IoT from the perspective of industrial applications, in particular, the major pillars in order to build an IoT application, i.e., architectural and cloud computing. This enabled readers to understand the concept of the IIoT and to identify the starting point. A case study of the Amazon Web Services Machine Learning (AML) platform for the chamfer length prediction of deburring processes is presented. An experimental setup of the deburring process and steps that must be taken to apply AML practically are also presented.

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Section: Research Motivation Experimental Setup and Data Acquisitionmentioning

confidence: 99%

Section: Webpage Developmentmentioning

confidence: 99%

An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0

et al. 2018

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“…In machining processes, ensuring the quality of a finished product is crucial and with advances in manufacturing technology, a need exists to integrate in-process monitoring technology into the production environment, so as to avoid manufacturing induced anomalies [ 1 , 2 , 3 ]. Advanced process monitoring technology coupled with an intelligent decision making support system can reduce the time taken to otherwise perform rework on finished components with defects.…”

Section: Introductionmentioning

confidence: 99%

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

Pappachan¹,

Caesarendra²,

Tjahjowidodo

et al. 2017

Sensors

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Process monitoring using indirect methods relies on the usage of sensors. Using sensors to acquire vital process related information also presents itself with the problem of big data management and analysis. Due to uncertainty in the frequency of events occurring, a higher sampling rate is often used in real-time monitoring applications to increase the chances of capturing and understanding all possible events related to the process. Advanced signal processing methods are used to further decipher meaningful information from the acquired data. In this research work, power spectrum density (PSD) of sensor data acquired at sampling rates between 40–51.2 kHz was calculated and the corelation between PSD and completed number of cycles/passes is presented. Here, the progress in number of cycles/passes is the event this research work intends to classify and the algorithm used to compute PSD is Welch’s estimate method. A comparison between Welch’s estimate method and statistical methods is also discussed. A clear co-relation was observed using Welch’s estimate to classify the number of cycles/passes. The paper also succeeds in classifying vibration signal generated by the spindle from the vibration signal acquired during finishing process.

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Adaptation to Industry 4.0 Using Machine Learning and Cloud Computing to Improve the Conventional Method of Deburring in Aerospace Manufacturing Industry

Caesarendra

Wijaya

Pappachan³

et al. 2019

2019 12th International Conference on Information &Amp; Communication Technology and System (ICTS)

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Event Classification from Sensor Data using Spectral Analysis in Robotic Finishing Processes

Cited by 3 publications

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An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0

An AWS Machine Learning-Based Indirect Monitoring Method for Deburring in Aerospace Industries Towards Industry 4.0

Frequency Domain Analysis of Sensor Data for Event Classification in Real-Time Robot Assisted Deburring

Adaptation to Industry 4.0 Using Machine Learning and Cloud Computing to Improve the Conventional Method of Deburring in Aerospace Manufacturing Industry

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