Industrial Platform for Rapid Prototyping of Intelligent Diagnostic Systems

Żabiński, Tomasz; Mączka, Tomasz; Kluska, Jacek

doi:10.1007/978-3-319-60699-6_69

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…This issue is particularly important for the ongoing work [19,20] over an innovative prediction tool for failure using mathematical models selected autonomously by an intelligent algorithm based on information criteria, as well as forecast errors and forecast error indicators. As well as the work [31] aimed at developing algorithms for real-time diagnostics and predictions of the state of technological processes.…”

Section: Discussionmentioning

confidence: 99%

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Assessment model of cutting tool condition for real-time supervision system

Kozłowski¹,

Mazurkiewicz²,

Żabiński³

et al. 2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Further development of manufacturing technology, in particular machining requires the search for new innovative technological solutions. This applies in particular to the advanced processing of measurement data from diagnostic and monitoring systems. The increasing amount of data collected by the embedded measurement systems requires development of effective analytical tools to efficiently transform the data into knowledge and implement autonomous machine tools of the future. This issue is of particular importance to assess the condition of the tool and predict its durability, which are crucial for reliability and quality of the manufacturing process. Therefore, a mathematical model was developed to enable effective, real-time classification of the cutting blade status. The model was verified based on real measurement data from an industrial machine tool.

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Section: Discussionmentioning

confidence: 99%

“…In order to develop a classifier, 2173 signals (series of acoustic pressure) obtained from microphone were analyzed, where 937 cases were concerned for blunt cutters and 1236 for sharp cutters. Each series x t j t n { } ≤ ≤ ) [31]. Exemplary realization of signal and sequence of correlation is depicted on Figure 1.…”

Section: Numerical Examplementioning

confidence: 99%

Assessment model of cutting tool condition for real-time supervision system

Kozłowski¹,

Mazurkiewicz²,

Żabiński³

et al. 2019

Eksploatacja i Niezawodność – Maintenance and Reliability

Self Cite

View full text Add to dashboard Cite

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“…The data acquisition system uses a platform for rapid prototyping of intelligent diagnostic systems [26] developed by the authors of the paper. The platform includes Beckhoff Industrial Computer C6920 (IPC) and distributed input/output system based on EtherCAT protocol.…”

Section: Testbed and Experiments Descriptionmentioning

confidence: 99%

“…where c 2 , c 3 , c 4 and c 5 are integer constant values corresponding to the start and end of the frequency range. The authors used multiple features determined in both frequency [26], [25], and time domains [32] to solve similar data classification problems. For features in the frequency domain, the entire frequency interval was subdivided into many smaller bands, including the intervals determined by values c 2 , ..., c 5 , in which different features were defined, i.a.…”

Section: Input Data Preparation For Aann Implementationmentioning

confidence: 99%

FPGA-Embedded Anomaly Detection System for Milling Process

et al. 2021

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The main goal of this work is to design a supervising controller able to detect an anomaly in the milling process and implement the soultion in Field Programmable Gate Array (FPGA) chip. Executing this task, the controller continuously monitors the vibration signal coming from the acceleration sensor, installed on the milling machine, and striving to isolate new vibration patterns which are different from typical patterns recorded for the correct milling process. The detection method relies on determining selected signal features in the frequency domain and applying an auto-associative neural network (AANN) for novelty detection. It has been shown that by exercising the frequency spectrum of the vibration signal, extracting specific features of the signal's spectrum, and using an auto-associative neural network, it is possible to detect anomalies in a milling process with relatively high efficiency. The accuracy, sensitivity, specificity, precision, and false alarm rate are equal to 94.3, 100, 91.2, 88.9, and 8.8 percent. All necessary calculations can be accomplished by the developed single-chip FPGA embedded supervising controller. The controller allows high-speed calculations under low power consumption. It characterizes high reliability and low price compared to typically encountered solutions.

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“…Also, a kernel‐based approach using Support Vector Data Description for novelty detection has been proposed for machinery components used in the industrial domain (Wang, Yu, Lapira, & Lee, 2013). Żabiński, Mączka, and Kluska (2017) applied LOF and neural network (NN) besides OCSVM. In order to detect anomalies in smart factory concept, intelligent solutions based on NNs (Nguyen, Van Ma, & Kim, 2018; Sonntag et al, 2017) are proposed.…”

Section: Related Workmentioning

confidence: 99%

Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

2020

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Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking. K E Y W O R D S acoustic anomaly detection, audio feature extraction, convolutional autoencoder, convolutional long short-term memory autoencoder, industrial processes 1 | INTRODUCTION The usage of smart systems in homes, factories, cities, and so forth, become more popular to ease the life of humans, especially in surveillance and monitoring tasks. Therefore, a wide variety of sensory information of different type and nature stemming from vision, audition, force/torque, temperature, energy consumption, power, network, and so forth, are individually or jointly utilized in monitoring tasks. However, processing the signals in real time is a challenging problem for abnormal event detection in dynamically changing environments. The aim of anomaly detection is to distinguish abnormal events from the usual ones. For the new generation of industrial manufacturing systems, monitoring of production with a focus on anomalies is one of the significant capabilities, since abnormal events can affect the quality of manufactured products, deteriorate the continuity and the reliability of the processes and assets (Panfilenko, Poller, Sonntag, Zillner, & Schneider, 2016). Even worse, some anomalies in production processes can endanger the safety of people who use industrial machines in the

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Industrial Platform for Rapid Prototyping of Intelligent Diagnostic Systems

Cited by 14 publications

References 6 publications

Assessment model of cutting tool condition for real-time supervision system

Assessment model of cutting tool condition for real-time supervision system

FPGA-Embedded Anomaly Detection System for Milling Process

Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

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