FPGA based Monitoring Platform for Condition Monitoring in Cylindrical Grinding

Humphreys, Ivor; Eisenblätter, Gerrit; O’Donnell, Garret E.

doi:10.1016/j.procir.2014.03.022

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Those systems support applications such as: CM, control, online documentation, trending, maintenance check lists and training through simulation videos, weather, sea conditions, voyage data [59,60]. The state-of-theart in ship CM data processing falls within one of two embedded system categories [21,60]:…”

Section: Related Workmentioning

confidence: 99%

Vibration Edge Computing in Maritime IoT

Michala

Vourganas

Coraddu

2021

ACM Trans. Internet Things

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IoT and the Cloud are among the most disruptive changes in the way we use data today. These changes have not significantly influenced practices in condition monitoring for shipping. This is partly due to the cost of continuous data transmission. Several vessels are already equipped with a network of sensors. However, continuous monitoring is often not utilised and onshore visibility is obscured. Edge computing is a promising solution but there is a challenge sustaining the required accuracy for predictive maintenance. We investigate the use of IoT systems and Edge computing, evaluating the impact of the proposed solution on the decision making process. Data from a sensor and the NASA-IMS open repository were used to show the effectiveness of the proposed system and to evaluate it in a realistic maritime application. The results demonstrate our real-time dynamic intelligent reduction of transmitted data volume by without sacrificing specificity or sensitivity in decision making. The output of the Decision Support System fully corresponds to the monitored system's actual operating condition and the output when the raw data are used instead. The results demonstrate that the proposed more efficient approach is just as effective for the decision making process.

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Section: Related Workmentioning

confidence: 99%

Vibration Edge Computing in Maritime IoT

Michala

Vourganas

Coraddu

2021

ACM Trans. Internet Things

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“…Also, the application proposed in [6] focusses on dedicated imagers in nuclear medicine as opposed to SANNs, the focus of the current paper. Another FPGA-based monitoring platform has included an FPGA for the purpose of signal processing and controlling multiple sensor channels in a machine condition monitoring system [7]. Here the FPGA doesn't include any form of soft processor, but instead controls a number of monitoring channels while processing data at the same time.…”

Section: Introductionmentioning

confidence: 99%

FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware

Karim

Harkin

McDaid

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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Spiking Astrocyte-neuron Networks (SANNs) model the adaptive/repair feature of the human brain. They integrate astrocyte cells with spiking neurons to facilitate a distributed and fine-grained self-repair capability at the synapse level. SANNs are more complex with the addition of astrocyte cells and require longer simulation times, as they are dynamic over much longer timescales than traditional neural networks. Therefore, dedicated FPGA accelerators offer reductions in simulation times. To support the acceleration of SANNs, the capability of fault injection to synapses and monitoring significant levels of neuron and astrocyte data for off-chip transmission to PC-based analysis, are required. This paper presents an FPGA-based monitoring platform (FMP) for injecting faults and capturing and analyzing data acquired from the SANN FPGA accelerator, Astrobyte. The FMP uses custom logic and a NIOS II based system to control fault injection and data monitoring on the FPGA. Results show accurate accelerated simulations of fault injection scenarios using FMP with speedups up to 65 times greater compared with equivalent Matlab implementations.

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“…Mokbel et al also used the AE sensors, but with a difference of using Fast Fourier transform (FFT) for analyzing the AE signals [7]. Humphreys et al applied field programmable gate array (FGPA) for acquiring and processing vibration signals, and applied feature extraction techniques for the condition monitoring [8]. However, high expertise required is one of the major limitations associated with this approach.…”

mentioning

confidence: 99%

Image Processing Based Tailor - made Software Package for the Condition Monitoring of Grinding Wheel

Gopan¹,

Tjprc²

2018

IJMPERD

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Condition monitoring of the grinding wheel is very significant in the industry as it plays a direct role in determining the final surface quality of the grounded components. But most of the current research in the field is focused on qualitative assessment of the grinding wheel condition. Application of machine vision and image processing are becoming more common in the manufacturing sector today. Various image processing techniques can be successfully utilized for distinguishing the loaded portion of the grinding wheel from the rest of the wheel. Here, we present the development and validation of an innovative software system based on image processing for evaluating quantitatively the percentage of grinding wheel loading. The software converts the RGB image captured using a USB microscope into the grayscale image that has pixel light intensity varying from 0 to 255. The high pixel intensity of the loaded chips in the grinding wheel is used for setting a threshold value for image segmentation (global thresholding). Based on the threshold value set, the software converts the grayscale image into a binary image of white and black pixels in which the black pixels corresponds to the loaded portion of the wheel. The percentage of wheel loading is determined as the ratio of black pixels to the total number of pixels. The results were validated with the equivalent Matlab® package and shows close agreement. This innovative, economical system has the capability of providing a reliable solution for the quantitative measurement of grinding wheel loading.

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FPGA based Monitoring Platform for Condition Monitoring in Cylindrical Grinding

Cited by 6 publications

References 10 publications

Vibration Edge Computing in Maritime IoT

Vibration Edge Computing in Maritime IoT

FPGA-based Fault-injection and Data Acquisition of Self-repairing Spiking Neural Network Hardware

Image Processing Based Tailor - made Software Package for the Condition Monitoring of Grinding Wheel

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