Application of vibration analysis using time-frequency analysis to detect and predict mechanical failure during the nut manufacturing process

Novelo, Xux Ek’ Azucena; Chu, Hsiao-Yeh

doi:10.1177/16878132221082758

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…The worst-case scenario is lured into unmanageable damaging scenarios, such as leaks and explosions, that can endanger thousands of people's lives or cause enormous ecological disasters. To avoid similar problems, industries add advanced techniques for monitoring the state of their infrastructures, vibration analysis, for example, is a procedure that monitors the operational status of active industrial equipment using data analytic methods [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Spiking Neural Network Anomaly Detection Method

Liu

Qiu

2022

Computational Intelligence and Neuroscience

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Cyber-attacks on specialized industrial control systems are increasing in frequency and sophistication, which means stronger countermeasures need to be implemented, requiring the designers of the equipment in question to re-evaluate and redefine their methods for actively protecting against advanced mass cyber-attacks. The attacks in question have huge motivations, ranging from corporate espionage to political targets, but in any case, they have a substantial financial impact and severe real-world implications. It should also be said that it is challenging to defend against cyber threats because a single point of entry can be enough to destroy an entire organization or put it out of business. This paper examines threats to the digital security of vibration monitoring systems used in petroleum infrastructure protection services, such as pipelines, pumps, and tank farms, where malicious interventions can cause explosions, fires, or toxic releases, with incalculable economic and environmental consequences. Specifically, a deep spiking neural network anomaly detection method is presented, which models the spike sequences and the internal presentation mechanisms of the information to discover with very high accuracy anomalies in vibration analysis systems used in oil infrastructure protection services. This is achieved by simulating the complex structures of the human brain and the way neural information is processed and transmitted. This work uses a particularly innovative form of the Galves–Löcherbach Spiking Model (GLSM) [1], which is a spiking neural network model with intrinsic stochasticity, ideal for modeling complex spatiotemporal situations, which is enhanced with possibilities of exploiting confidence intervals by modeling optimally stochastic variable-length memory chains that have a finite state space.

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Section: Introductionmentioning

confidence: 99%

A Deep Spiking Neural Network Anomaly Detection Method

Liu

Qiu

2022

Computational Intelligence and Neuroscience

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“…By converting the signals from the time domain to the frequency domain, the user can observe a particular defect’s development with respect to an increase in the amplitude of a given frequency [ 78 ]. Novelo et al [ 79 ] presented several analytical techniques on vibration data, i.e., Fourier transform, empirical mode decomposition, multiscale entropy (MSE), shock response spectrum and correlation to identify the fault indicators in a nut die tool. Empirical mode decomposition is a method of frequency decomposition widely used in structural health monitoring (SHM) for damage detection.…”

Section: Signal Processingmentioning

confidence: 99%

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Weinert

Tormey

O’Hara

et al. 2023

Sensors

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Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector.

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“…For steel bridges, bolt-loosening usually leads to bolt joint failures, which consequently causes collapse [4,5]. Factory errors during bolt manufacturing could also be responsible for early damage when in use [6]. The Federal Highway Administration (FHWA) proposed physical visual supervision of bridges to avoid bridge collapse due to bolt-loosening [7], however the accuracy of such approach was affected by human errors [8].…”

Section: Introductionmentioning

confidence: 99%

Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

Adamu

Yan³

et al. 2022

Sustainability

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The early detection of bolts and nuts’ loss on bridges has a huge tendency of averting bridge collapse. The aim of this research is to develop a novel framework for the detection of bolt–nut losses in steel bridges using deep learning techniques. The objectives include: to design a framework for the detection of nuts and bolts and nut holes using deep learning techniques, to implement the designed framework using Python programming, and to evaluate the performance of the designed framework. Convolutional neural network (CNN) and long- and short-term memory (LSTM) techniques were employed using 8 × 8 blocks of images of bridges as inputs. Based on the proposed models, which considered the CNN in its ordinary form, and combined with the LSTM and You Only Look Once (YOLOv4) algorithms, the CNN achieved average classification accuracy of 95.60% and the LSTM achieved an accuracy of 93.00% on the sampled images. The YOLOv4 algorithm, which is a modified version of the CNN with single forward propagation, was utilized, and the detection accuracy was 76.5%. The relatively high level of detection accuracy recorded by the CNN is attributed to its stepwise extraction by convolution and pooling processes. However, a statistical test of the hypothesis at the 5.0% level of significance revealed that there was no statistically significant difference between object detection and classifications among the models used in the built framework. Therefore, the use of the CNN model is recommended for the detection of nuts and bolts and nut holes on steel truss bridges for effective structural health monitoring (SHM) purposes based on its high level of detection accuracy and speed.

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Application of vibration analysis using time-frequency analysis to detect and predict mechanical failure during the nut manufacturing process

Cited by 7 publications

References 32 publications

A Deep Spiking Neural Network Anomaly Detection Method

A Deep Spiking Neural Network Anomaly Detection Method

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

Detection of Nut–Bolt Loss in Steel Bridges Using Deep Learning Techniques

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