Emergency-Awareness System for Vibration Sensing, Monitoring, and Safety

International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378 (Online), Volume-13 Issue-11, October 2025 12 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262713121125 DOI:10.35940/ijese.L2627.13111025 Journal Website: www.ijese.org Abstract: An emergency-awareness system for vibration sensing, monitoring, and safety is proposed in this paper. The presented electronic system comprises a central monitoring unit and three vibration-sensing modules. They are connected to an Ethernet network for rapidly transmitting the monitored data and issuing command signals. Each vibration-sensing module consists of a compact 3-axis micro-electro-mechanical system (MEMS) accelerometer and three high-resolution 24-bit sigma-delta analogue-to-digital converters (ADCs). The proposed sensor modules can be attached to infrastructure and machines (e.g., toxic-gas valves, seismic monitors) in industrial factories. Upon detecting a tiny 4-gal vibration from a high-resolution sensing module, it immediately reports to the central monitoring unit via the network and issues commands to shut off the gas valves. Because the presented system leverages high internet transmission speeds (much faster than vibration waves propagating through infrastructure) and high-resolution sensor modules, safety measures (e.g., switching off toxic-gas valves) can be implemented immediately before the arrival of the vibration waves. Keywords: Emergency Awareness, Micro-Electro-Mechanical System (MEMS), Vibration Sensing. Nomenclature: MEMS: Micro-Electro-Mechanical System ADCs: Analogue-to-Digital Converters MEMS: Micro-Electro-Mechanical System BPDN: Basis Pursuit Denoising FBG: Fibre Bragg Grating CNN: Convolutional Neural Network FPGA: Field Programmable Gate Array SPI: Serial Peripheral Interface I. INTRODUCTION For monitoring and safety considerations, studies on vibration and applications are reported [1]-[4]. In [1], a continuity-modified basis pursuit denoising (BPDN) algorithm is proposed to reconstruct vibration signals and obtain the order spectra. In [2], a wireless alarm microsystem self-powered by a vibration-threshold-triggered energy harvester is presented. A wireless alarming signal induced by an abnormal vibration event can be autonomously transmitted to a point over 2 km away when the vibratory excitation lasts for 1 minute. Manuscript received on 30 September 2025 | Revised Manuscript received on 08 October 2025 | Manuscript Accepted on 15 October 2025 | Manuscript published on 30 October 2025. *Correspondence Author(s) To-Po Wang*, Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan. Email ID: [email protected], ORCID ID: 0000-0003-4696-2259 Zi-Ping Kuo, Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan. Email ID: [email protected] © The Authors. Published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/ Three types of modules with threshold accelerations of 2g, 2.6g, and 3g are designed and fabricated. In [3], a method for identifying different milling states in a robotic surgical system is introduced to acquire and process vibration signals. The tissue vibration signal is measured with a laser displacement sensor, and an accelerometer records the power device's acceleration signal. According to the experiment results, 20-g accelerations were recorded. In [4], a new current-aided vibration-order-tracking method for bearing-fault diagnosis of variable-speed direct-drive wind turbines is presented. The technique explores a simple, practical approach to vibration order tracking. An acquisition system with a 10-kHz sampling frequency was used, and a wind turbine with an inner-race fault was measured in the fundamental frequency range of 50 to 75 Hz. In the literature [2]-[4], vibrations are characterised by accelerations exceeding 2g (2000 gal) or frequencies exceeding 50 Hz. For this study, the proposed emergency-awareness system is designed to detect tiny 4-gal seismic vibrations with frequencies below 50 Hz. The use of seismic accelerometers for vibration sensing is presented in [5]-[7]. In [5], a fibre three-component accelerometer based on an ultrashort fibre Bragg grating (FBG) array for seismic exploration in oil and gas wells is proposed and demonstrated. The accelerometer consists of three orthogonal sensing units, each based on a spring-mass structure with a cylindrical casing that is resistant to temperatures up to 155 ℃ and pressures up to 50 MPa. In [6], an experimental procedure for moving-person detection is developed. The sensitivity and weight of the seismic transducer with a piezoelectric membrane are 400 mV/g over 3-200 Hz and 50g, respectively. Moreover, the seismic signal processing unit performs filtering, amplification, processing, and analysis. The digitisation of the input analogue signal is performed at 600 Hz. In [7], a method for detecting elephant movement by analysing ground vibration recordings from seismic sensors is introduced. A convolutional neural network (CNN)-based model named “GajGamini” is used to track the elephant movements. To accurately capture the vibration waves, we developed a new high-resolution emergency-awareness system. The proposed system consists of a 3-axis MEMS accelerometer with a high sensitivity of 420 mV/g. Due to its small bulk size and high sensitivity, the MEMS accelerometer is suitable for emergency-awareness industrial systems. To provide sufficient time for safety measures to be implemented, the proposed high-resolution 24-bit sigma-delta ADC circuits are used for vibration detection. To-Po Wang, Zi-Ping Kuo Emergency-Awareness System for Vibration Sensing, Monitoring, and Safety Emergency-Awareness System for Vibration Sensing, Monitoring, and Safety 13 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262713121125 DOI:10.35940/ijese.L2627.13111025 Journal Website: www.ijese.org II. PROPOSED SYSTEM The proposed system consists of a central monitoring server and three vibration-sensing modules connected via a high-speed computer network, as shown in Fig. 1. Because the Ethernet network is fast, the data transmission rate is much higher than the propagation speed of vibration waves through the infrastructure. This speed advantage can be used to lift on-wafer probes and to switch off gas valves before the incoming vibration waves arrive. The sensing module comprises a 3-axis accelerometer, three 24-bit analogue-to-digital converters. (ADCs) for high accuracy, a data buffer, an ARM core for data processing, and an Ethernet controller for data transmission. Vibration-Wave Sensing Module (A) Data Buffer 24-Bit Analog-to-Digital Converter ARM Core Ethernet 3-Axis Accelerometer Vibration-Wave Sensing Module (B) Internet Ethernet Vibration-Wave Sensing Module (C) Central Monitoring Unit [Fig.1: Block Diagram of the Proposed EmergencyAwareness Industrial-Electronic System for Vibration Sensing, Monitoring, and Safety] An outline of the proposed emergency-awareness sensing module is shown in Fig. 2. As shown in Fig. 2(a), the sensing module dimensions are 15 cm × 8.5 cm × 5.5 cm. Fig. 2(b) depicts a top view of a sensing module with an ARM development board. The bottom view of a sensing module is presented in Fig. 2(c). It consists of a compact 3-D MEMS accelerometer, three ADC drivers, three high-resolution 24-bit sigma-delta ADCs, and a data transmission circuit with an index generator in the FPGA. An ARM microprocessor (MPU) is used in this work to process seismic wave data. The peripherals of the MPU can include a USB host, image-capture hardware, an LCD panel, a multimedia card, secure data I/O, etc. Therefore, the MPU must deal with many programs in sequence. To continue monitoring seismic waves, the sensed analogue data from a compact 3-D MEMS accelerometer are digitised in real time by high-resolution 24-bit sigma-delta ADCs. These digital data must be processed and transferred in real time to a central monitoring unit via a computer network. To prevent data loss caused by differences in data processing speeds between the ADCs and MPU, a high-speed, high-reliability data transmission circuit in the FPGA is proposed in Fig. 3. This circuit is implemented in a field-programmable gate array (FPGA) due to its flexible design. The FPGA has a serial peripheral interface (SPI) for receiving digitised data from the ADC. In addition, a memory size of 128 bits x 512 words is implemented as a data buffer for reliable storage of the digitized data, and the interface between the memory and ARM MPU is constructed for robust data transmission, including the sections of addressing, reading registers, memory output registers, chip selection, and clock control. (a) (b) (c) [Fig.2: Proposed Emergency-Awareness Sensing Module, (a) Dimension, (b) Top View, and (c) Bottom View] Dual Port Memory 128 Bits x 512 Shift-In Register Shift Out Register Register Memory Memory Out Register Address Writing Register Address Reading Register FSO Control CS Control SCK Control SYNC Control ADC Reset Control 128 96 128 128 9 9 From AD7765 SCO (=MCLK/2=20 MHz) To AD7765 Reset From AD7765 SDO To AD7765 SYNC To ARM MISO From ARM CS From ARM SCK Word Count 32 FPGA From AD7765 FSO [Fig.3: Block Diagram of the Proposed Data Transmission Circuit with Index Generator (Word Count) in FPGA] Fig. 4 shows an earthquake wave detected by the proposed system. Upon detection of an acceleration greater than 4.0 gal around the time of 0:31:09, it immediately reports to the central monitoring unit and issues commands to switch off the solenoid gas valve shown in Fig. 5. The measured overall system response time is 75 ms, resulting in 1,525 ms (=1,600ms-75ms) for safety before the occurrence of 18-gal peak motion. International Journal of Emerging Science and Engineering (IJESE) ISSN: 2319–6378 (Online), Volume-13 Issue-11, October 2025 14 Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) © Copyright: All rights reserved. Retrieval Number:100.1/ijese.L262713121125 DOI:10.35940/ijese.L2627.13111025 Journal Website: www.ijese.org 0:31:09 0:31:10 0:31:11 0:31:12 0:31:13 0:31:14 0:31:15 0:31:16 0:31:17 0:31:18 0:31:19 0:31:20 0:31:21 0:31:22 0:31:23 1.6 sec (=1600 ms) 75 ms: turn off solenoid gas valve Acceleration (gal) 20 16 12 8 4 0 [Fig. 4: Detected Earthquake wave and Measured System Response Time to Switch off the Solenoid Gas Valve] Gas Valve Swith Off [Fig.5: Gas Valve is (a) on, and (b) off] III. CONCLUSION A high-performance, emergency-aware industrial electronic system for vibration sensing, monitoring, and safety has been developed and validated. Featuring a compact 3-D MEMS accelerometer, a high-resolution 24-bit sigma-delta ADC, and a data transmission circuit in an FPGA. It has the advantage of high internet transmission speed (much faster than vibration waves propagating through infrastructure), so safety measures (e.g., switching off gas valves) can be implemented before the arrival of the vibration waves (e.g., when the threshold voltage is exceeded). Compared to other works in the literature, this work not only achieves high performance but can also be adopted in industrial environments for safety. ACKNOWLEDGMENT This work is supported in part by the National Science and Technology Council of Taiwan, R.O.C. DECLARATION STATEMENT After aggregating input from all authors, I must verify the accuracy of the following information as the article's author. ▪ Conflicts of Interest/ Competing Interests: Based on my understanding, this article has no conflicts of interest. ▪ Funding Support: This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence. ▪ Ethical Approval and Consent to Participate: The content of this article does not necessitate ethical approval or consent to participate with supporting documentation. ▪ Data Access Statement and Material Availability: The adequate resources of this article are publicly accessible. ▪ Author’s Contributions: The authorship of this article is contributed equally to all participating individuals. REFERENCES 1. A. Palko and L. Sujbert, “Recursive compressive sensing for vibration analysis,” IEEE Trans. On Instrumentation and Measurement. Vol. 74, 2025. DOI: http://doi.org/10.1109/TIM.2025.3612656 2. Q. Tang, Q. He, M. Li, C. Dong, D. Xu, and X. Li, “Wireless alarm microsystem self-powered by vibration-threshold-triggered energy harvester,” IEEE Trans. Ind. Electron., vol. 63, no. 4. pp. 2447-2456, Apr. 2016. DOI: http://doi.org/10.1109/TIE.2015.2510503 3. Y. Dai, Y. Xue, and J. Zhang, “Milling state identification based on vibration sense of a robotic surgical system,” IEEE Trans. Ind. Electron., vol. 63, no. 10. pp. 6184-6193, Oct. 2016. DOI: http://doi.org/10.1109/TIE.2016.2574981 4. J. Wang, Y. Peng, and W. Qiao, “Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines,” IEEE Trans. Ind. Electron., vol. 63, no. 10. pp. 6336-6346, Oct. 2016. DOI: http://doi.org/10.1109/TIE.2016.2571258 5. X. Li, F. Geng, X. Liu, S. Li, T. Duan, R. Wang, F. Chen, and X. Qiao, “Fibre-optic three-component accelerometer for seismic exploration in oil and gas wells based on ultrashort fibre Bragg grating arrays,” IEEE Trans. On Instrumentation and Measurement. Vol. 74, 2025. DOI: http://doi.org/10.1109/TIM.2025.3586338 6. P. P. Fastykovsky and Y. L. Lepikh, “Remote compact seismic sensor for the moving person detection,” IEEE Sensors Letters, vol. 4, no. 8, Aug. 2020. DOI: http://doi.org/10.1109/LSENS.2020.3007831 7. Chandan, M. Chakraborty, S. Anchal, B. Mukhopadhyay, and S. Kar, “GajGamini: mitigating man-animal conflict by detecting moving elephants using ground vibration-based seismic sensor,” IEEE Sensors Letters, vol. 8, no. 9, Sept. 2024. DOI: http://doi.org/10.1109/LSENS.2024.3442830 AUTHOR’S PROFILE To-Po Wang received the PhD degree from National Taiwan University, Taipei, Taiwan. In 2009, he joined the faculty of the Department of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan, where he is currently a professor. His research interests are integrated circuits and applications. Zi-Ping Kuo received an M.S. degree from the Graduate Institute of Electronic Engineering, National Taipei University of Technology, Taipei, Taiwan. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of the Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)/ journal and/or the editor(s). The Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.