The energy harvesting (EH) from unused natural waste energy sources is common nowadays because of rising power demand. The sources have the potential of producing micro to milliwatts power depending on the ambient conditions. Many researchers have been concentrating on micro-level energy harvesting to provide power to the micro-devices in a remote area. The concept leads to a drastic reduction in cost. Once the structure is established, it can generate electricity with minimal cost or effort like renewable sources. This paper reviews the two key areas of the piezoelectric energy harvesting system (PEHS), namely, mechanical and electronic approaches, developed by several researchers. From the thorough review, it is realized that the existing technologies more or less can capable to EH by using the piezoelectric elements; however, the consistency and stability of the systems are not up to the mark yet. In this study, vibration-based PEHS has been considered for the application of the optimization technique to enhance its performance. This review has been focused on numerous challenges and recommendations for next-generation EH by utilizing vibration-based piezoelectric elements.

Review of piezoelectric energy harvesting system and application of optimization techniques to enhance the performance of the harvesting system

On the use of metasurface for Vortex-Induced vibration suppression or energy harvesting

The fault detection and diagnosis (FDD) along with condition monitoring (CM) and of rotating machinery (RM) have critical importance for early diagnosis to prevent severe damage of infrastructure in industrial environments. Importantly, valuable industrial equipment needs continuous monitoring to enhance the safety, reliability, and availability and to decrease the cost of maintenance of modern industrial systems and applications. However, induction motor (IM) has been extensively used in several industrial processes because it is cheap, reliable, and robust. Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system. Thus, many current methods based on different techniques are employed as a fault prognosis and diagnosis of rolling elements bearing of IM. Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains. Artificial intelligence (AI) techniques have proven their significance in every field of digital technology. Industrial machines, automation, and processes are the net frontiers of AI adaptation. There are quite developed literatures that have been approaching the issues using signals and data processing techniques. However, the key contribution of this work is to present an extensive review of CM and FDD of the IM, especially for rolling elements bearings, based on artificial intelligent (AI) methods. This study highlights the advantages and performance limitations of each method. Finally, challenges and future trends are also highlighted.

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A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Self-sustained autonomous wireless sensing based on a hybridized TENG and PEG vibration mechanism

This paper presents a novel autonomous method for condition monitoring of rotating machines during operation based on radio frequency (RF) pulse transmission using energy harvesting from operational vibration. An energy harvesting unit is designed to generate and rectify the energy harvested from the machine vibration using Voltage Multiplier (VM) circuit and to store the energy into a capacitor. Then, this energy harvesting unit runs a smart system consisting of a microcontroller and the RF transmitter designed to send a pulse at specific capacitor voltage. A pulse-based condition monitoring approach is introduced which monitors the state of the machine during the operation. In order to estimate power output of the piezoelectric harvester for a realistic vibration signal, the Fourier Transform concept for signal decomposition is incorporated into the well-known electromechanical distributed parameter model. Using experimental data, performance of this autonomous condition monitoring system is tested for a water pump at different conditions. To do so, acceleration data from a centrifugal water pump are acquired with an accelerometer, which then decomposed into a series of harmonics using Fast Fourier Transform. Then using analytical distribute model, a bimorph energy harvester with two Piezoceramic layers is optimized to generate maximum power from the water pump vibration. Consequently, the condition monitoring of the water pump is performed using the presented pulse-based approach. Results of this study show that, the fault diagnosis can be performed autonomously by applying the pulse-based method presented in this work, and by using the piezoelectric harvesting device as an energy source.

A novel method for autonomous remote condition monitoring of rotating machines using piezoelectric energy harvesting approach

A broadband macro-fiber-composite piezoelectric energy harvester for higher energy conversion from practical wideband vibrations

A comprehensive electromechanically coupled model for non-uniform piezoelectric energy harvesting composite laminates

Damping plays a critical role in power generation by piezoelectric energy harvesting, and yet there is a lack of sensitivity studies on different sources of damping. In this paper, two damping sour...

Majid Khazaee

Papers

A novel method for autonomous remote condition monitoring of rotating machines using piezoelectric energy harvesting approach

A broadband macro-fiber-composite piezoelectric energy harvester for higher energy conversion from practical wideband vibrations

A comprehensive electromechanically coupled model for non-uniform piezoelectric energy harvesting composite laminates

Effect of Damage and Support Damping Mechanisms on Unimorph Piezoelectric Energy Harvester

The investigation of viscous and structural damping for piezoelectric energy harvesters using only time-domain voltage measurements