Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

Patil, Sonali S.; Pardeshi, Sujit S.; Patange, Abhishek D.

doi:10.32604/cmes.2023.025516

Cited by 9 publications

(7 citation statements)

References 44 publications

(43 reference statements)

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“…The YOLO model is faster, with lower computational requirements, and more easily accessible when considering the real-time requirements of welding spark detection in our proposed system. Object detection in YOLO is conceived as a regression problem [ 55 ] and provides the class probabilities of the detected images [ 53 , 56 ]. The process of tuning the automotive HSV parameters consists of three steps: the HSV loop finder, YOLO welding spark detector in color masks, and confidence score sum-up.…”

Section: Methodsmentioning

confidence: 99%

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Jin,

Ahn,

Kim

et al. 2023

Sensors

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One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regard to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real-time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue saturation value. Additional filtering methods regarding the non-welding zone and a contour area-based filter were also newly developed to enhance the accuracy of welding spark prediction. The method was evaluated using 230 welding spark images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding spark detection to enhance fire surveillance at construction sites.

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Section: Methodsmentioning

confidence: 99%

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Jin,

Ahn,

Kim

et al. 2023

Sensors

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“…where N t and N f represent the numbers of time samples and frequency bins, respectively. For the pairs (3,6) and (2,20), the range is set to [N t /8 : 4 : N t /4], while for the pairs (3,8) and (2,30), it is set to [N t /4 : 4 : 3N t /8], as suggested in prior work [33].…”

Section: Adaptive Time-frequency Distributionsmentioning

confidence: 99%

Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals

Jurdana,

Baressi Šegota

2024

Mathematics

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Frequency-modulated (FM) signals, prevalent across various applied disciplines, exhibit time-dependent frequencies and a multicomponent nature necessitating the utilization of time-frequency methods. Accurately determining the number of components in such signals is crucial for various applications reliant on this metric. However, this poses a challenge, particularly amidst interfering components of varying amplitudes in noisy environments. While the localized Rényi entropy (LRE) method is effective for component counting, its accuracy significantly diminishes when analyzing signals with intersecting components, components that deviate from the time axis, and components with different amplitudes. This paper addresses these limitations and proposes a convolutional neural network-based (CNN) approach for determining the local number of components using a time–frequency distribution of a signal as input. A comprehensive training set comprising single and multicomponent linear and quadratic FM components with diverse time and frequency supports has been constructed, emphasizing special cases of noisy signals with intersecting components and differing amplitudes. The results demonstrate that the estimated component numbers outperform those obtained using the LRE method for considered noisy multicomponent synthetic signals. Furthermore, we validate the efficacy of the proposed CNN approach on real-world gravitational and electroencephalogram signals, underscoring its robustness and applicability across different signal types and conditions.

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“…The paper also considered the application of these constructs to the solution of several motivating parallel programming problems. In [ 39 ], the authors acquired spindle vibrations corresponding to correct and incorrect (with defects) cutter configurations in real time. These data were transformed to the time-frequency domain and further processed by the proposed architectures in the graphical form, i.e., spectrogram.…”

Section: Application Of the Concept Of Multi-category Object Classifi...mentioning

confidence: 99%

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Prażnowski

Mamala

Deptuła

et al. 2023

Sensors

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This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada–Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.

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Health Monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

Cited by 9 publications

References 44 publications

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Welding Spark Detection on Construction Sites Using Contour Detection with Automatic Parameter Tuning and Deep-Learning-Based Filters

Convolutional Neural Networks for Local Component Number Estimation from Time–Frequency Distributions of Multicomponent Nonstationary Signals

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

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