Preventive maintenance optimization policy based on a three-stage failure process in finite time horizon

In this paper, a preventive maintenance optimization policy based on a three-stage failure process in finite time horizon is proposed. The lifetime of system is divided into three stages by the concept of three-stage failure process, which is corresponding to the three color scheme commonly used in industry. The subsequent inspection interval is halved when the minor defective stage is identified. Once identifying the severe defective stage, maintenance action is carried out. A numerical example is presented to demonstrate the efficiency of the proposed models

A joint optimal policy of inspection and age based replacement based on a three-stage failure process

Preventive maintenance (PM) and condition-based maintenance (CBM) are two dominant maintenance policies in industrial applications. Inspection activities are the foundation of PM and CBM policies as to provide the operating information of system through processing the collected vibration data. Age based replacement is one of the most used preventive maintenance policy aiming at avoiding unplanned downtime and higher failure loss. This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process for a single component system. The three-stage failure process, which is closer to reality, divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the severe defective stage is identified, maintenance action is carried out immediately. The system is replaced once it reaches certain age. However, two potential actions are considered and analyzed in this paper when the minor defective stage is identified: halving the subsequent inspection interval or replacing the item immediately. As inspection may not be perfect because of the complexity of plant items, both perfect and imperfect inspection cases are considered. Finally, a case study is presented to demonstrate the efficiency of the proposed models

Energy Harvesting in Flexible and Semi-transparent Hydrogenated Amorphous Silicon Solar Cells

The goal of this study is to design, characterize, and fabricate efficient hydrogenated amorphous silicon (a-Si:H) photovoltaic (PV) modules, and semi-transparent solar cells on thin, mechanically flexible, optically transparent plastic substrates for energy harvesting applications. The cells are deposited on thin flexible plastics at low temperature (120~ 150 °C). In the first part of study, a-Si:H n-i-p solar cells were fabricated using a plasma enhanced chemical vapor deposition (PECVD) and the deposition conditions were optimized to maximize their efficiency. To improve light absorption, we engineered the front window layer by optimizing p-layer thickness and bandgap (Eg). The best a-Si:H n-i-p solar cell showed open circuit voltage (Voc) of 0.67 V, short circuit current density (Jsc) of 7.92 mA/cm2, fill factor (FF) of 53.73 %, and energy conversion efficiency of 2.86 %. Using developed deposition recipes, the a-Si:H PV modules were designed and fabricated on a 10 by 10 cm2 polyethylene-naphthalate (PEN) substrate which consists of 72 rectangular cells. The sub-cells were connected in series forming eight strings with connection pads at the ends, so that the strings of 18 sub-cells were connected in parallel using the external blocking diodes. The typical a-Si:H PV module showed Voc of 12.78 V, Jsc of 8 mA/cm2, FF of 53.8 %, and average efficiency of 3.05 %. The PV module performance is similar to that of individual solar cells, which means good scalability of our module fabrication process. In the second part, a-Si:H n-i-p solar cells were inverted to fabricate a-Si:H p-i-n solar cells. In this device structure, p-type buffer-layer was introduced to improve the interface between aluminum doped zinc oxide (AZO)/p-layer. The optimum device showed Voc of 0.885 V, Jsc of 8.88 mA/cm2, FF of 52.01 %, and efficiency of 4.09 %. In the last part of this study, semi-transparent solar cells were fabricated both on glass and plastic substrates to demonstrate feasibility of building integrated photovoltaics (BIPV), based on the a-Si:H p-i-n cells in the second part. To overcome the mechanical stress inside films between AZO and plastic, the barrier-layer coating was used to prevent the delamination which is frequently encountered between plastic substrate and transparent conductive oxide (TCO) layer. Our semi-transparent a-Si:H solar cells showed the efficiency of 4.98 % and 4.77 % for the cells fabricated on glass and plastic substrates, respectively. In addition, the semi-transparent a-Si:H p-i-n solar cell was also used as radiation detector within the visible part of the spectrum. From the Ne spectral lines, the micro-plasma spectral from radiation detector obtained response comparable with fiber optic detector

A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation

© 2016 Peidong Liang et al. We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG) collected from human operator's arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM) method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios

Spare support model based on gamma degradation process

Spare parts ordering is very important in the domain of system support based on condition-based maintenance. For a single-unit system with condition monitoring, a joint degradation and spare parts ordering model is established in this paper to achieve the lowest total cost rate as the objective. The degradation process of system is assumed to be followed a gamma process. A decision on optimal spare ordering time by the improved cost rate model based on the proposed degradation model is made. Finally, a case analysis is implemented to demonstrate the effectiveness of the proposed model in this paper. Analysis results show that the proposed model can reduce the cost rate effectively

An availability model based on a three-stage failure process under age based replacement

This paper proposes a joint optimal policy of inspection and age based replacement based on a three-stage failure process to jointly optimize the inspection and replacement intervals. The three-stage failure process divides the failure process of system into three stages: namely normal, minor defective and severe defective. When the minor defective stage is identified, the subsequent inspection interval is halved. Once identifying the severe defective stage, the maintenance action is carried out immediately. The system is replaced once it reaches the certain age. Finally, a numerical example is presented to demonstrate the efficiency of the proposed model

Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning

Deep learning techniques have dominated the literature on aspect-based sentiment analysis (ABSA), yielding state-of-the-art results. However, these deep models generally suffer from spurious correlation problems between input features and output labels, which creates significant barriers to robustness and generalization capability. In this paper, we propose a novel Contrastive Variational Information Bottleneck framework (called CVIB) to reduce spurious correlations for ABSA. The proposed CVIB framework is composed of an original network and a self-pruned network, and these two networks are optimized simultaneously via contrastive learning. Concretely, we employ the Variational Information Bottleneck (VIB) principle to learn an informative and compressed network (self-pruned network) from the original network, which discards the superfluous patterns or spurious correlations between input features and prediction labels. Then, self-pruning contrastive learning is devised to pull together semantically similar positive pairs and push away dissimilar pairs, where the representations of the anchor learned by the original and self-pruned networks respectively are regarded as a positive pair while the representations of two different sentences within a mini-batch are treated as a negative pair. To verify the effectiveness of our CVIB method, we conduct extensive experiments on five benchmark ABSA datasets and the experimental results show that our approach achieves better performance than the strong competitors in terms of overall prediction performance, robustness, and generalization