Condition monitoring can reduce machine breakdown losses, increase productivity and operation safety, and therefore deliver significant benefits to many industries. The emergence of wireless sensor networks (WSNs) with smart processing ability play an ever-growing role in online condition monitoring of machines. WSNs are cost-effective networking systems for machine condition monitoring. It avoids cable usage and eases system deployment in industry, which leads to significant savings. Powering the nodes is one of the major challenges for a true WSN system, especially when positioned at inaccessible or dangerous locations and in harsh environments. Promising energy harvesting technologies have attracted the attention of engineers because they convert microwatt or milliwatt level power from the environment to implement maintenance-free machine condition monitoring systems with WSNs. The motivation of this review is to investigate the energy sources, stimulate the application of energy harvesting based WSNs, and evaluate the improvement of energy harvesting systems for mechanical condition monitoring. This paper overviews the principles of a number of energy harvesting technologies applicable to industrial machines by investigating the power consumption of WSNs and the potential energy sources in mechanical systems. Many models or prototypes with different features are reviewed, especially in the mechanical field. Energy harvesting technologies are evaluated for further development according to the comparison of their advantages and disadvantages. Finally, a discussion of the challenges and potential future research of energy harvesting systems powering WSNs for machine condition monitoring is made.

Helical gearboxes play a critical role in power transmission of industrial applications. They are vulnerable to various faults due to long-term and heavy-duty operating conditions. To improve the safety and reliability of helical gearboxes, it is necessary to monitor their health conditions and diagnose various types of faults. The conventional measurements for gearbox fault diagnosis mainly include lubricant analysis, vibration, airborne acoustics, thermal images, electrical signals, etc. However, a single domain measurement may lead to unreliable fault diagnosis and the contact installation of transducers is not always accessible, especially in harsh and dangerous environments. In this article, a Compressive Sensing (CS)-based Dual-Channel Convolutional Neural Network (CNN) method was proposed to accurately and intelligently diagnose common gearbox faults based on two complementary non-contact measurements (thermal images and acoustic signals) from a mobile phone. The raw acoustic signals were analysed by the Modulation Signal Bispectrum (MSB) to highlight the coupled modulation components relating to gear faults and suppress the irrelevant components and random noise, which generates a series of two-dimensional matrices as sparse MSB magnitude images. Then, CS was used to reduce the image redundancy but retain key information owing to the high sparsity of thermal images and acoustic MSB images, which significantly accelerates the CNN training speed. The experimental results convincingly demonstrate that the proposed CS-based Dual-Channel CNN method significantly improves the diagnostic accuracy (99.39% on average) of industrial helical gearbox faults compared to the single-channel ones.

Thanks to the fast development of micro-electro-mechanical systems (MEMS) technologies, MEMS accelerometers show great potentialities for machine condition monitoring. To overcome the problems of a poor signal to noise ratio (SNR), complicated modulation, and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers, a triaxial MEMS accelerometer-based on-rotor sensing (ORS) technology was developed in this study. With wireless data transmission capability, the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high SNR. The orthogonal outputs lead to a construction method of analytic signals in the time domain, which is versatile in fault detection and diagnosis of rotating machines. Two case studies based on an induction motor were carried out, which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods. Comparatively, vibration signals from translational on-casing accelerometers are less capable of detecting such faults. This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines.

This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.

Electromechanical systems as the prime power source are widely employed in industry. To ensure the high productivity and safety of the motor-gear system, motor current signature analysis (MCSA) becomes a cost-effective and effective approach to health condition monitoring of motors and gears simultaneously. Generally, working conditions of the motor-gear system can be separated into stationary and nonstationary working conditions. The nonstationary working conditions usually refer to varying speeds, which have been broadly investigated in the angular domain analysis and time-frequency analysis. The stationary working conditions are assumed to have a constant rotating speed which is an ideal scenario but practically the rotating speed varies slightly with randomness. The random speed variation seems neglectable but it actually spreads the energy into adjacent frequency bins, which thus attenuates the amplitude of fault signatures and leads to inaccurate fault diagnosis. To address this issue, a Squeezed Modulation Signal Bispectrum (MSB) approach is developed to concentrate the leaked energy for accurately diagnosing gear faults with motor current signals. The Squeezed MSB concentrates the energy in the frequency domain along the time axis to overcome the random speed oscillation induced energy leakage and then, demodulates and aligns the modulation fault signatures from the squeezed spectra for ensemble averaging to further enhance fault signatures. The simulation study shows the performance of the proposed method under different levels of random speed variation and the experimental studies demonstrate the effectiveness of the Squeezed MSB for diagnosing gear tooth breakage faults under a wide range of working conditions.