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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. • Using two complementary non-contact measurements to overcome the instability and inaccuracy of a single domain signal. • Reducing the image redundancy and capacity to significantly accelerate the training speed through Compressive Sensing. • The proposed Compressive Sensing-based Dual-Channel CNN method achieves accurate and efficient gearbox fault diagnosis. [ABSTRACT FROM AUTHOR]

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time–frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions. • The frequency domain TKEO is proposed for processing the single carrier signal. • The fault extraction capabilities of CMS and Fast-SC are enhanced using TKEO. • The optimized methods have high computational efficiency. • The results validate the effectiveness of the proposed methods for IM fault diagnosis. [ABSTRACT FROM AUTHOR]

Identifying a spectral frequency band with abundant fault information from spectral coherence is essential for improved envelope spectrum-based bearing diagnosis. Both blind features and targeted features have been employed to distinguish informative spectral frequency band of spectral coherence. However, how to select appropriate feature to correctly discriminate the optimal frequency band of spectral coherence in different scenarios is problematic. In this study, a new targeted feature is presented to quantify the signal-to-noise ratio in narrow frequency bands of spectral coherence, and further a method based on the proposed feature is developed to distinguish an optimal spectral frequency band of spectral coherence for bearing diagnostics. The efficiency of the developed method, typical blind feature-based methods and typical targeted feature-based methods in identifying the defect-sensitive frequency band of spectral coherence and bearing fault diagnosis is validated and compared using simulated signals with different interference noises and bearing experimental signals. The advantages and limitations of typical blind and targeted feature-based methods in different scenarios are summarized to guide the application. The results demonstrate that the developed targeted feature can efficiently evaluate bearing failure information in the cyclic frequency domain, and the presented approach can accurately discriminate the failure-related spectral frequency band of spectral coherence and detect different bearing faults compared with the methods based on the state-of-the-art features. • FDSNR is proposed to evaluate the fault information in the frequency domain. • Performance of typical blind and targeted features-based IESFOgram is investigated. • Characteristics of typical feature metrics in selecting ISFB of SCoh are provided. • The IESFOgram based on DFSNR exhibit excellent bearing diagnostic performance. [ABSTRACT FROM AUTHOR]

Transient impulses caused by local defects are critical for the fault detection of rotating machines. However, they are extremely weak and overwhelmed in the strong noise and harmonic components, making the transient features are very difficult to be extracted. This paper proposes an adaptive multi-scale improved differential filter (AMIDIF) to enhance the identification of transient impulses for rotating machine fault diagnosis. In this scheme, firstly, the AMIDIF is performed to decompose the measured signal of rotating machine into a series of multi-scale improved differential filter (MIDIF) filtered signals. Subsequently, in view of the MIDIF filtered signals exhibit varying extents of validity in revealing fault features, a weighted reconstruction method using correlation analysis is proposed in which the weighted coefficients are counted and distributed to the corresponding MIDIF filtered signals to highlight the effective MIDIF filtered signals and weaken the invalid ones. Finally, the transient impulse components of rotating machinery are obtained by multiplying the weighted coefficients and the MIDIF filtered signals under different scales. Furthermore, the fault types of rotating machines are inferred from the fault defect frequencies in the envelope spectrum of the transient impulses. Simulation analysis and experimental studies are implemented to verify the performance of the AMIDIF compared with the state-of-the-art methods including spectral kurtosis (SK), multi-scale average combination different morphological filter (ACDIF) and multi-scale morphology gradient product operation (MGPO). The results prove that the AMIDIF has excellent performance in extracting transient features for rotating machines fault diagnosis. • An AMIDIF is developed for transient impulse enhancement. • AMIDIF can extract the bidirectional impulses in the signal at the same time. • Correlation coefficient is used to optimize the weighted coefficient in AMIDIF. • Performance of the AMIDIF is validated by simulation and experimental cases. [ABSTRACT FROM AUTHOR]

Transient impulses are important information for machinery fault diagnosis. However, the transient features contained in the vibration signals generated by planetary gearboxes are usually immersed by a large amount of background noise and harmonic components. Even mathematical morphology (MM) is an excellent anti-noise signal processing method that can directly extract the geometry of impulse features in the time domain, but the four basic operators of MM can only extract one-way impulses while cannot extract the bidirectional impulses effectively at the same time. To accurately extract the impulse feature information, a novel method for fault detection of planetary gearbox based on an enhanced average (EAVG) filter and modulated signal bispectrum (MSB) is proposed. Firstly, the properties of the extracted impulses based on the four basic operators of MM will be divided into two categories of enhanced average operators. The four EAVG filters consist of the average weighted combination of enhanced average operators, and then the best EAVG filter is selected based on correlation coefficient to implement on the original vibration signal. It allows EAVG filter to extract positive and negative impulses of vibration signal, thereby improving the accuracy of planetary gearbox fault detection. Subsequently, the performance of the EAVG filter is influenced by the length of its structural element (SE), which is adaptively determined using an indicator based kurtosis. Then, the EAVG filter selects the optimal SE length to eliminate the interference of background noise and harmonic components to enhance the impulse components of the vibration signal. However, the nonlinear modulation components that are related to the fault types and severities are not extracted exactly and still remained in the filtered signal by EAVG. Finally, the MSB is utilized to the EAVG filtered signal to decompose the modulated components and extract the fault features. The advantages of EAVG over average (AVG) filter are clarified in the simulation study. In addition, the EAVG-MSB is validated by analyzing the vibration signals of planetary gearboxes with sun gear chipped tooth, sun gear misalignment and bearing inner race fault. The results indicate that the EAVG-MSB is effective and accurate in feature extraction compared with the combination morphological filter-hat transform (CMFH) and average combination difference morphological filter (ACDIF), and the feasibility of the EAVG-MSB are proved for planetary gearbox condition monitoring and fault diagnosis. • EAVG filter is used to extract the positive and negative impulses. • Kurtosis is taken as a novel criterion to optimize the SE length of EAVG. • MSB is used to extract fault feature for planetary gearbox fault detection. • Experimental results prove that the EAVG-MSB outperforms the ACDIF and CMFH. [ABSTRACT FROM AUTHOR]

Abstract Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model. Graphical abstract Highlights • A BN-based approach is proposed for the fault isolation of mechanical systems. • A procedure is proposed for simplifying the BN-based diagnostic model. • The computational efficiency is improved by simplifying the BN diagnostic model. • A novel structure of BN is proposed for assigning prior probability. [ABSTRACT FROM AUTHOR]

In analyzing signals from a wind turbine gearbox this paper suggests a new signal processing procedure named as CMF-EEMD method which is formed by applying conventional EEMD to a new type of combined mode function (CMF). This CMF consists of a low frequency CMF, denoted as C L , and a high frequency CMF, denoted as C h . Then it optimizes the amplitude of the added noise in decomposing C h and C L using EEMD. Finally, it calculates cyclic autocorrelation function (CAF) for every characteristic IMF from EEMD. The proposed procedure is applied to analyze the multi-faults of a wind turbine gearbox and the results confirm better performances in resolving different signal components by the proposed method than that from the cyclic autocorrelation function (CAF) of a direct EEMD analysis. [ABSTRACT FROM AUTHOR]

Abstract This article introduces a revised common trend framework to monitor nonstationary and dynamic trends in industrial processes and shows needs for each improvement on the basis of three application studies. These improvements relate to (i) the extension of the common trend framework to include sets that contain stationary and nonstationary variables, (ii) handling cases where residuals are not drawn from multivariate normal distributions and (iii) the application of the framework to larger variable sets. Existing work does not adequately address these practically important issues. Industrial application studies highlight the needs for (i) the extended framework to model data sets containing stationary and nonstationary variables, (ii) handling statistics that are not based on normally distributed residuals and (iii) the use of Chigira procedure to robustly extract common trends. The extended framework is compared to traditional approaches. [ABSTRACT FROM AUTHOR]

• A novel method for pressure prediction and cavitation evaluation of water jacket in engines is proposed. • The pressure amplification factor for a thin liquid layer under modal vibration is derived. • The nonlinear relationship between liner vibration and coolant pressure is investigated to reveal the mechanism of vibration-induced cavitation. • Effects of acoustic parameters and liner modal parameters on cavitation are discussed. • The correctness and rationality of the method is verified by experiment. Vibrations of the cylinder liner in internal combustion engines cause coolant cavitation, inducing cavitation erosion. Owing to the lack of a comprehensive understanding of vibration-induced cavitation, the cavitation erosion has been long an unresolved problem influencing engine lifetime and reliability and concerned more in recent years due to lightweight designs. This study proposes a novel pressure prediction method that considers the dynamic properties of a cylinder liner and the pressure amplification effect of a thin water jacket. A pressure amplification factor for a thin water jacket, defined as the ratio of the acoustic pressure to the plane progressive wave pressure generated by the same liner vibration, was derived for the first time. The pressure amplification mechanism in the thin water jackets was revealed. The cavitation risk area on the cylinder liner was predicted, and the effects of acoustic parameters and liner modal properties on cavitation were analysed. The theoretical analyses agreed with the experimental results, proving the accuracy of the proposed method. Moreover, it has found that the thin-layer configuration of the water jacket significantly reduced the vibration threshold for triggering cavitation. Vibration-induced cavitation in cylinder liners is closely related to the liner constraint modes and acoustic properties of the water jackets. The research results enrich the theoretical understanding of vibration-induced cavitation and provide theoretical support for the prediction and mitigation of cavitation erosion in internal combustion engines. [Display omitted] [ABSTRACT FROM AUTHOR]

Intelligent transportation necessitates advanced perception and cognitive systems that can provide continuous feedback from the vehicle. However, sensors relying on batteries face challenges such as high maintenance costs and environmental issues due to the limited lifespan of the power source. To overcome these challenges, this paper reports an efficient battery-free solution for transportation monitoring. The solution utilizes a speed-amplified rotary energy harvester (SAREH) to power various wireless Bluetooth sensors, enabling continuous monitoring of the vehicle's motion state. The SAREH combines a contra-rotating mechanism with a friction pendulum, resulting in excellent power output in a compact design. Experimental results demonstrate the ability of SAREH to extract power from vehicles operating at speeds ranging from 180 to 1260 rpm. The maximum power output and corresponding power density are measured as 712 mW and 34 mW cm−3, respectively. The prototype successfully powers portable electronics and supports battery-free navigation, triaxial acceleration, and temperature multi-sensors during real road and railway simulation tests. Additionally, the SAREH operates as a highly sensitive speed sensor and an early-warning system for detecting the vehicle's motion state. These results represent a significant advancement in intelligent transportation systems by showcasing the practicality of self-powered wireless monitoring capabilities on vehicles. [Display omitted] • A novel speed-amplified energy harvesting technique is proposed. • The proposed system incorporates contra-rotating and friction pendulum mechanisms. • The prototype enables installation in confined spaces while achieving a high power density of up to 34 mW cm−3. • The prototype simultaneously powers daily electronics and supports battery-free wireless sensors. • The prototype operates as a speed sensor and an early-warning system for detecting the motion state of the vehicle. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mesh stiffness model of opening-crack tooth with elastoplastic deflection is built. • The model can accurately estimate the mesh stiffness of gears with crack failure. • Impacts of failure severity on the mesh stiffness and load sharing are analyzed. • Tooth deflection affects mesh stiffness, load sharing and meshing zone ratio. In the gear transmission system, tooth root crack often occurs due to the impact of machining technology and cyclic load. The appearance of tooth root cracks will affect its mesh stiffness and change the vibration characteristics of the system, thereby reducing system reliability and service life. In previous studies on the modeling of the mesh stiffness of cracked gears, the cracks were often supposed to be in a closed state, and the elastoplastic deflection of the fault gear teeth was also neglected, resulting in significant errors in the estimation of the mesh stiffness. When a tooth root crack occurs, the engaged gear teeth will deviate from the theoretical meshing position due to the elastoplastic deflection of the fault tooth. Therefore, an accurate mesh stiffness estimation model in view of the opening crack and elastoplastic deflection of the cracked tooth is put forward in this study. Firstly, the actual meshing position of the tooth pairs is derived considering the elastoplastic deformation in the crack opening state; Then, the improved potential energy method is adopted to calculate the mesh stiffness, and the finite element method (FEM) is applied to verify it. At last, the influence of failure severity and elastoplastic deflection degree on mesh stiffness and load sharing is analyzed. The results show that the severity of the crack failure and elastoplastic deflection has a significant impact on the mesh stiffness and load-bearing. This research can provide stiffness input for the study of fault dynamics of the gear transmission system. [ABSTRACT FROM AUTHOR]

With an increasing application of clinching in different industrial fields, the demand for a better understanding of the knowledge of static and dynamic characteristics of the clinched joints is required. In this paper, the clinching process and tensile–shear failure of the clinched joints have been numerically simulated using finite element (FE) method. For validating the numerical simulations, experimental tests on specimens made of aluminium alloy have been carried out. The results obtained from tests agreed fairly well with the computational simulation. Tensile–shear tests were carried out to measure the ultimate tensile–shear strengths of the clinching joints and clinching-bonded hybrid joints. Deformation and failure of joints under tensile–shear loading were studied. The normal hypothesis tests were performed to examine the rationality of the test data. This work was also aimed at evaluating experimentally and comparing the strength and energy absorption of the clinched joints and clinching-bonded hybrid joints. [ABSTRACT FROM AUTHOR]