Search Results (7)

When performing the milling process, it is essential to consider the life estimation and availability of the milling tool to achieve a reliable and optimized result at a lower cost. It is necessary to monitor the tool’s condition during the milling process due to its inherent wear nature. In earlier times, visual inspection was used to assess the condition of the milling tool, and it was considered a complex and specialized task. Due to this issue, the milling process requires further investigation. In the manufacturing and automation industry, deteriorated milling tools have led to several challenges, including a decline in product quality, reduced equipment utilization, and increased costs. The tool wear prediction is a challenging and complex task, as it includes several variables. The existing framework for tool condition monitoring, in terms of the degree, typically falls short in terms of real-time prediction and accuracy. Hence, in this research, a tool-life estimation model is developed to minimize unexpected failures during the milling process using deep learning techniques. Initially, the data are collected from benchmark sources. The statistical features, deep features via fuzzy autoencoders (FAEs), and t-Distributed Stochastic Neighbor Embedding (t-SNE)-based features are extracted from the input data to capture various information related to the machine. These features are passed to the proposed multi-head cross-covariance attention fusion-based dilated dense bi-directional gated recurrent unit (MCF-DD-BiGRU) for accurate prediction of tool life. The input features are fused using a multi-head cross-covariance attention mechanism to enhance the representation of interdependencies among features. The DBi-GRU network processes the fused features to improve the accuracy of tool-life prediction for milling machines. The prediction efficiency of the implemented model is compared with the existing models to ensure its effectiveness. Full article

Biogas contributes to environmental protection by reducing greenhouse gas emissions and promoting the recycling of organic waste. Its utilization plays a crucial role in addressing the challenges of climate change and sustainability. However, the deterioration of process plants involved in biogas production due to corrosion has a critical impact on the safety and durability of their operations. In order to maintain the safety of structures in terms of service life with respect to corrosion, it is essential to develop effective corrosion engineering control methods. Electrochemical techniques have become a useful tool by which to evaluate corrosion resistance. However, these techniques may require microscopic analysis of the material surface and the analysis may be influenced by subjective factors. To solve this drawback, this work proposes the use of SVM models to predict the corrosion status of the material used in biogas production with no need to perform microscopic analysis after the electrochemical test. The obtained results of sensitivity and specificity equal to 0.94 and 0.97, respectively, revealed the utility of the proposed stochastic models to assure the corrosion state of the equipment involved in biogas production. SVM-based models are an effective alternative for accurately evaluating material durability and comparing the corrosion resistance of different materials in biogas environments. This approach facilitates the selection of the most suitable material to achieve greater durability and long-term performance. Synopsis: The results show that the proposed model is a useful tool to predict the behaviour of stainless steel against corrosion according to the environmental conditions to which the material is exposed in biogas production. Full article

This paper addresses the problem of joint production and preventive maintenance (PM) planning of a deteriorating manufacturing system generating greenhouse gas (GHG) emissions. The system is composed of a deteriorating machine, subject to random failures and repairs evolving in a dynamic and stochastic context. The main objective is to develop control policies that minimize the sum of backlog, inventory, maintenance, and emission costs. The stochastic optimal control theory based on the dynamic programming approach is used to obtain the optimality conditions and the optimal control policies, which are determined using numerical methods. Sensitivity analyses are provided to depict and validate the obtained structure of the production and PM policies characterized by multiple thresholds that jointly regulate the production and PM rates with the age, emissions, and inventory levels. Furthermore, we compared the performance of the obtained control policies with that of the most relevant policies found in the literature and showed their superiority by considerable cost savings. Finally, the proposal’s implementation is provided to equip managers of the considered manufacturing system with an effective and robust decision-support tool. Full article

Falls by the elderly pose considerable health hazards, leading not only to physical harm but a number of other related problems. A timely alert about a deteriorating gait, as an indication of an impending fall, can assist in fall prevention. In this investigation, a comprehensive comparative analysis was conducted between a commercially available mobile phone system and two wristband systems: one commercially available and another representing a novel approach. Each system was equipped with a singular three-axis accelerometer. The walk suggestive of a potential fall was induced by special glasses worn by the participants. The same standard machine-learning techniques were employed for the classification with all three systems based on a single three-axis accelerometer, yielding a best average accuracy of 86%, a specificity of 88%, and a sensitivity of 86% via the support vector machine (SVM) method using a wristband. A smartphone, on the other hand, achieved a best average accuracy of 73% also with an SVM using only a three-axis accelerometer sensor. The significance analysis of the mean accuracy, sensitivity, and specificity between the innovative wristband and the smartphone yielded a p-value of 0.000. Furthermore, the study applied unsupervised and semi-supervised learning methods, incorporating principal component analysis and t-distributed stochastic neighbor embedding. To sum up, both wristbands demonstrated the usability of wearable sensors in the early detection and mitigation of falls in the elderly, outperforming the smartphone. Full article

Preventive maintenance is widely used in wind turbine equipment to ensure their safe and reliable operation, and this mainly includes time-based maintenance (TBM) and condition-based maintenance (CBM). Most wind farms only use TBM as the main maintenance strategy in engineering practice. Although this can meet certain reliability requirements, it cannot fully utilize the characteristics of TBM and CBM. For this, a state model based on the stochastic differential equation (SDE) is established in this paper to describe the spatio-temporal evolution process of the degradation behavior of wind turbine generators, in which the components’ failure is represented by a proportional hazards model, the random fluctuation of the state is simulated by the Brownian motion, and the SDE model is solved by a function transformation method. Based on the model, the characteristics of TBM and CBM, and the asymptotic relationship between them, are discussed and analyzed, the necessity and feasibility of their combination are expounded, and a joint maintenance strategy is proposed and analyzed. The results show that the stochastic model can better reflect the real deterioration state of the generator. Moreover, TBM has a fixed maintenance interval, depending on global sample tracks and, only depending on the local sample track, CBM can follow the component state. Finally, the rationality and effectiveness of the proposed model and results are verified by a practical example. Full article

Currently, the Remaining Useful Life (RUL) prediction accuracy of stochastic deterioration equipment is low. Existing researches did not consider the impact of imperfect maintenance on equipment degradation and maintenance decisions. Therefore, this paper proposed a remaining useful life prediction-based maintenance decision model under data-driven to extend equipment life, promoting sustainable development. The stochastic degradation model was established based on the nonlinear Wiener process. A combination of real-time update and offline estimation estimated the degradation model’s parameters and deduced the equipment’s RUL distribution. Based on the RUL prediction results, we established a maintenance decision model with the lowest long-term cost rate as the goal. Case analysis shows that the model proposed in this paper can improve the accuracy of RUL prediction and realize equipment sustainability. Full article

Kinematic laser scanning with moving platforms has been used for the acquisition of 3D point clouds of our environment for many years. A main application of these mobile systems is the acquisition of the infrastructure, e.g., the road surface and buildings. Regarding this, the distance between laser scanner and object is often notably shorter than 20 m. In the close range, however, divergent incident laser light can lead to a deterioration of the precision of laser scanner distance measurements. In the light of this, we analyze the distance precision of the 2D laser scanner Z + F Profiler 9012A, purpose-built for kinematic applications, in the range of up to 20 m. In accordance with previous studies, a clear dependency between scan rate, intensity of the backscattered laser light and distance precision is evident, which is used to derive intensity-based stochastic models for the sensor. For this purpose, a new approach for 2D laser scanners is proposed that is based on the static scanning of surfaces with different backscatter. The approach is beneficial because the 2D laser scanner is operated in its normal measurement mode, no sophisticated equipment is required and no model assumptions for the scanned surface are made. The analysis reveals a lower precision in the range below 5 m caused by a decreased intensity. However, the Z + F Profiler 9012A is equipped with a special hardware-based close range optimization partially compensating for this. Our investigations show that this optimization works best at a distance of about 2 m. Although increased noise remains a critical factor in the close range, the derived stochastic models are also valid below 5 m. Full article