Greater demands for underwater sound absorption materials have been growing due to the concern about underwater noise control in water. Among the range of existing materials, polymer-based materials ...are increasingly being utilized as underwater sound absorption materials. In this paper, different kinds of polymer-based materials for underwater sound absorption with regards to key factors associated with sound absorption properties, measurements, applications, and mechanisms are reviewed and summarized. Commonly used polymers for underwater sound comprise, in general, interpenetrating polymer networks (IPN), polymer foams, and gradient polymers. To further improve underwater sound absorption performance, different types of inclusions that are introduced into the polymer matrix to transform the polymers as underwater sound absorption materials via air voids, solid inclusions, nanofillers, and phononic crystals are discussed. Challenges for further development of better polymer-based acoustic materials to meet requirements of current and future underwater applications are also presented.
•Key factors and tests of polymer-based underwater sound absorption materials.•Different polymers with or without inclusions for underwater sound absorption.•Mechanisms behind the underwater sound absorption.•Challenges for further improving polymer-based underwater water sound absorption materials.
In this study, a machine-learning approach based on Gaussian process regression was developed to identify the optimized processing window for laser powder bed fusion (LPBF). Using this method, we ...found a new and much larger optimized LPBF processing window than was known before for manufacturing fully dense AlSi10Mg samples (i.e., relative density ≥ 99%). The newly determined optimized processing parameters (e.g., laser power and scan speed) made it possible to achieve previously unattainable combinations of high strength and ductility. The results showed that although the AlSi10Mg specimens exhibited similar Al-Si eutectic microstructures (e.g., cell structures in fine and coarse grains), they displayed large difference in their mechanical properties including hardness (118 - 137 HV 10), ultimate tensile strength (297 - 389 MPa), elongation to failure (6.3 - 10.3%), and fracture toughness (9.9 - 12.7 kJ/m2). The underlying reason was attributed to the subtle microstructural differences that were further revealed using two newly defined morphology indices (i.e., dimensional-scale index Id and shape index Is) based on several key microstructural features obtained from scanning electron microscopy images. It was found that in addition to grain structure, the sub-grain cell size and cell boundary morphology of the LPBF fabricated AlSi10Mg also strongly affected the mechanical properties of the material. The method established in this study can be readily applied to the LPBF process optimization and mechanical properties manipulation of other widely used metals and alloys or newly designed materials.
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Wear status identification including wear rate estimation and wear mechanism assessment can be performed using wear debris information. However, although on-line monitoring methods have distinctive ...advantages over off-line approaches, existing on-line monitoring methods provide limited features of wear particles and have difficulties characterising complex wear states. Most of them determine wear status based on changes in the wear rates, and the wear mechanisms are not taken into consideration. Therefore, comprehensive wear state identification is a bottleneck in real-time machine health monitoring for condition-based maintenance. In order to further advance on-line monitoring technology, this paper, in a case study format, presents a new approach for wear state characterisation using comprehensive wear debris features. For this purpose, wear experiments were carried out on a four-ball rig, and a particle imaging system was employed to capture videos of moving particles to acquire dynamic features. Based on this, wear particles were firstly counted to characterise wear rate. In this stage, a statistical clustering model was established using a mean-shift algorithm to categorise wear debris samples. A trend of wear state evolution was thus obtained. Secondly, the size, shape and colour of wear debris were extracted to identify particles into fatigue, sliding and oxides for wear mechanism analysis. The analysis results of wear mechanisms were related to the trend of the wear state. Correspondingly, a changing chart that contains the wear degree and wear mechanisms was drawn. Therefore, an on-line system has been developed to capture comprehensive particle information to assess the wear severity and mechanisms for in-depth wear analysis and full-life machine condition monitoring.
•Wear conditions including wear severity and wear mechanisms are characterised using wear particle features.•Moving wear particles are counted to describe wear rate.•The size, shape and colour of dynamic particles are extracted to analyse wear mechanisms.•Mean-shift algorithm is employed to build the model of wear state identification.
Lubricant film thickness is the most informative variable that reflects lubrication conditions and transmission efficiency in the mechanical equipment, therefore its measurement is highly important. ...Despite a large number of theoretical models that have been developed to describe the lubricant film, complexities and uncertainties in a real tribo-pair contact still hinder the implementation of accurate and robust methods of in-situ film thickness measurements. Recently, ultrasonic-based measurement has been widely studied, showing promising potential owing to its non-destructive characteristics, high sensitivity, and limited physical modifications. This paper comprehensively reviews basic principles of ultrasonic-based oil film measurement; summarizes progress on calculation models and associated signal processing methods; exhibits in-lab demonstrations and in-situ applications; and discusses key technical issues and possible solutions.
Ferrography plays an important role in wear analysis for machine condition monitoring, in which effective and efficient wear particle analysis is regarded as a crucial pre-requisite. An automatic ...wear particle detection and classification process is developed here using a cascade of two convolutional neural networks and a support vector machine (SVM) classifier. The neural networks are used for particle detection and recognition while particle classification is conducted in the SVM. This structure ensures that the computation expense is reduced and the accuracy is improved. The proposed network is verified using a large number of ferrograph images. Results show that high classification accuracies are obtained. Furthermore, the proposed approach can be further developed and applied in online machine condition monitoring applications.
•A novel convolutional neural network, called the WP-DRnet, is developed for automatic wear particle detection, recognition and classification.•Wear particle analysis by deep learning algorithm can reduce the errors caused by hand-crafted features determined by human experts.•The WP-DRnet is constructed with a cascade of two convolutional neural networks and a multi-SVM classifier.•The WP-DRnet is applied directly on the captured ferrograph images to reduce the computation expense and improve accuracy.
For the purpose of automatic wear debris classification, a hybrid convolution neural network (CNN) is used with transfer learning (TL) and support vector machine (SVM) to identify four types of wear ...debris including cutting, sphere, fatigue and severe sliding particles. Experimental results indicate that image features extracted from the CNN is more distinguishable than that acquired from the local binary pattern, the histogram of oriented gradients and the color-based methods. The classification accuracy and efficiency of the proposed hybrid CNN with TL and SVM is also higher than that of the CNN, the CNN with TL, and the CNN with SVM. This work provides an effective solution for automatic wear debris identification applicable for machine wear mechanism analysis.
•A hybrid CNN model is developed for automatic wear particle classification.•The classification model contains particle feature extraction and recognition, which improves the analysis accuracy and efficiency.•Weights and biases of the CNN are initialized with the ImageNet trained parameters to improve generalization.•A new wear particle classifier is built by augmenting the CNN and SVM to increase the classification accuracy.
The characteristics of wear debris particles are valuable information sources for machine condition monitoring. A possible approach is to apply ferrography with computer vision techniques. However, ...when images are captured on-line, it is observed that particles tend to appear agglomerated and an effective image processing method is hence required. A particle extraction procedure is here developed by making use of advances in morphological segmentations. The reliability of particle separation is improved with both transmitted and reflected debris images. Furthermore, an iterative morphological scaling operation, incorporating gray and boundary based segmentation, is included to increase segmentation accuracy. The performance of the proposed method is tested using real-world wear debris images captured from the lubricant return line in a gearbox. Particle characteristics are found to follow closely the Weibull distribution.
•Automatic wear debris separation algorithm based on on-line images was proposed.•Particles were separated by integrating morphological and grayscale features.•Results were verified by hypothesis test to follow a Weibull distribution.
•The relationships of the reflected signal and the incident signal are clarified.•The spectrum of the incident signal is obtained from the reflected signals.•A reconstruction algorithm of the ...incident signal is proposed.
The oil film thickness in an oil-lubricated tribo-pair can be measured using reflected ultrasonic waves and various transformation models. Conventionally, this approach requires the incident signal to be calibrated using off-line methods prior to testing, and this limits the on-line measurement of oil film thickness in sliding components such as journal bearings. To enable on-line calibration, we propose a method of reconstructing the incident signal from the easily obtained reflected signal. This involves analyzing the amplitude and phase spectrum of the reflection coefficient to reconstruct the incident signal. More specifically, the following main findings of the amplitude and phase of reflection coefficient at the resonance frequency are reported. 1) There is a zero-crossing at the resonance frequency, and therefore the phases of the incident and reflected signal are the same. 2) If part of the reflected signal is included for calculation, the frequency of the extreme point in the amplitude spectrum of the reflected signal is equal to the resonance frequency only when the extreme point phenomenon occurs at the center frequency of the transducer. At other positions, the frequency of the minimum amplitude in the amplitude spectrum of the reflected signal is not equal to the resonance frequency. Utilizing these relationships, the phase and amplitude of the incident signal can be accurately reconstructed by following the proposed method, provided the thickness of the oil film between the tribo-pairs can be widely ranged to cover both the blind and the resonance zones. Correspondingly, a practical schedule for the on-line reconstruction of the incident signal is proposed, which includes an operation to adjust the oil film thickness within the blind and the resonance zone. This method is validated on a test rig by comparing it with the traditional off-line methods.
Oil monitoring constitutes an important and essential component of condition monitoring technologies and has distinguished advantages in revealing wear, lubrication and friction conditions of ...tribo-pairs. Newly developed on-line/in-line oil monitoring technologies extend the merits into real-time applications and demonstrate significant benefits in maintenance and management of equipment. This paper comprehensively reviews the progress of on-line/in-line oil monitoring techniques including sensor technologies, their scopes and industrial applications. Based on the existing developments and applications of the sensors for oil quality and wear debris measurements, the trends for future sensor developments are discussed with focuses on accurate, integrated and intelligent features along with exploring a fundamental issue, that is, acquiring the knowledge on degradation mechanisms which has not received sufficient attention until now. Current status of applications of on-line oil monitoring is also reviewed. Although limited reports have been found on this topic, increasing awareness and encouraging progress in on-line monitoring techniques are recognized in applications such as aircraft, shipping, railway, mining, etc. Key fundamental issues for further extending the on-line oil monitoring techniques in industries are proposed and they include long-term reliability of sensors in harsh conditions, and agreement with fault or maintenance determination.
Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the ...efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.
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