Vibration-powered sensing system for engine condition monitoring

Weddell, Alex S.; Merrett, Geoff V.; Barrow, Stuart; Al-Hashimi, Bashir M.

doi:10.1049/cp.2012.0585

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…This accelerometer has a wide range of application, for some examples: in [91] they focus on characterizing the kinetic energy that can be harvested by a wireless node with an Internet of Things form factor and on developing energy allocation algorithms for such nodes; in [92] an application, design and implementation of a novel vibration-powered sensing system for condition monitoring of engines is presented; in [93], a self-powered smart tag for wireless SHM in aeronautical applications is presented; reference [94] presents in detecting induction motor faults caused due to electrical or mechanical origin is by vibration analysis; and in [95] a machine tool vibration fault monitoring system based on Internet of Things is studied and established.…”

Section: Adxl-345 Sensormentioning

confidence: 99%

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

Varanis

Silva

Mereles

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

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In this paper, the use of MEMS accelerometers for measuring mechanical vibrations is presented. Also a wide review of the literature is performed by presenting the uses of the MEMS accelerometers in a great number of applications. These sensors are known for their low prices, low power consumption and low sizes, which enhance their use in applications such as energy harvesters, monitoring processes and for educational purposes. In order to propose these sensors for measuring vibrations, a complete evaluation of the MEMS accelerometers was performed by measuring amplitudes and frequencies of oscillations and comparing their dynamic characteristics with other accelerometers with higher precision. Moreover, two experiments were conducted: In the first one, the measurements of the amplitude given by a MEMS and a standard accelerometer while being excited with sinusoidal waves with different frequencies using a vibration exciter were taken and compared. For the second experiment, three MEMS sensors and a piezoelectric accelerometer were used to measure the accelerations of a 3-DOF shear-building excited by an unbalanced DC motor. The signals obtained were compared in the time and frequency domains; for the last case, the wavelet transform, the wavelet coherence and the power spectrum density were used.

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Section: Adxl-345 Sensormentioning

confidence: 99%

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

Varanis

Silva

Mereles

et al. 2018

J Braz. Soc. Mech. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…Weddell, et al [30] was proposed a vibrationpowered sensing system for condition monitoring of machines. This system addresses the energy issues of wireless sensor nodes by proposed a vibration energy system accommodate variability in the power supply.…”

Section: Related Workmentioning

confidence: 99%

Integrated Wireless Technologies with Computer for Industrial Machinery Fault Diagnosis: Challenges Comparison and Characteristics: A Review

Lilo¹

2018

MJPS

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Condition monitoring of machinery in industries is becoming an emerging technique for efficient operations and productivity. A wide range of fault diagnosis approaches have been proposed to improve machinery operations in industries. These monitoring and fault diagnosis approaches are most effective for reliable machinery operations. However, due to the harsh environment of industries, these approaches have been suffered from different challenges. In order to extract the significant features for fault diagnosis and monitoring, different neural, fuzzy, and signal processing based systems were adopted. In this paper, we discuss wireless sensor based fault diagnosis and monitoring approaches and their types for industrial machinery. Furthermore, the paper presents industrial challenges to adopting these approaches and related efforts.

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“…The design points of the n runs are determined using the D-Optimal criterion as explained later in this section. Using matrix notation, equation (12) can be written as:ŷ = Xβ (13) where X r×p is r×p design matrix. The difference between the observed values y and fitted valuesŷ for the ith observation i = y i −ŷ i is called the residual for that specific observation.…”

Section: Fast Design Space Explorationmentioning

confidence: 99%

“…The design parameter here is the tuning controller duty cycle (10-30mins) and the performance indicator is the energy consumption. As frequency tuning control in real-life applications is still a major challenge at the forefront of research in energy harvesting [13], the tuning control was omitted from the simulation model for real vibration input and only used for the sine wave input. Fig.…”

Section: D) Energy Storagementioning

confidence: 99%

Fast Design Space Exploration of Vibration-Based Energy Harvesting Wireless Sensors

et al. 2013

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Abstract-An energy-harvester-powered wireless sensor node is a complicated system with many design parameters. To investigate the various trade-offs among these parameters, it is desirable to explore the multi-dimensional design space quickly. However, due to the large number of parameters and costly simulation CPU times, it is often difficult or even impossible to explore the design space via simulation. This paper presents a response surface model (RSM) based technique for fast design space exploration of a complete wireless sensor node powered by a tunable energy harvester. As a proof of concept, a software toolkit has been developed which implements the proposed design flow and incorporates either real data or parametrized models of the vibration source, the energy harvester, tuning controller and wireless sensor node. Several test scenarios are considered, which illustrate how the proposed approach permits the designer to adjust a wide range of system parameters and evaluate the effect almost instantly but still with high accuracy. In the developed toolkit, the estimated CPU time of one RSM estimation is 25µs and the average RSM estimation error is less than 16.5Index Terms-Tunable energy harvester, wireless sensor node, hardware description language, response surface model, simulation.

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Vibration-powered sensing system for engine condition monitoring

Cited by 10 publications

References 4 publications

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

MEMS accelerometers for mechanical vibrations analysis: a comprehensive review with applications

Integrated Wireless Technologies with Computer for Industrial Machinery Fault Diagnosis: Challenges Comparison and Characteristics: A Review

Fast Design Space Exploration of Vibration-Based Energy Harvesting Wireless Sensors

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