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In this paper, the problem of the huge direct and indirect losses caused by vehicle failure is discussed. To solve the problem, we focus on the brake system of automobile as the research object and classification of high and low reliability value according to the characteristics of different components. On this basis, the maintenance strategy and cost analysis formula are given. Finally, the Monte Carlo method is used to simulate the cost calculation on MATLAB.

PurposeThis research introduces an innovation strategy aimed at bolstering the reliability of a renewable energy resource, which is hybrid energy systems, through the application of a metaheuristic algorithm. The growing need for sustainable energy solutions underscores the importance of integrating various energy sources effectively. Concentrating on the intermittent characteristics of renewable sources, this study seeks to create a highly reliable hybrid energy system by combining photovoltaic (PV) and wind power.Design/methodology/approachTo obtain efficient renewable energy resources, system designers aim to enhance the system’s reliability. Generally, for this purpose, the reliability redundancy allocation problem (RRAP) method is utilized. The authors have also introduced a new methodology, named Reliability Redundancy Allocation Problem with Component Mixing (RRAP-CM), for optimizing systems’ reliability. This method incorporates heterogeneous components to create a nonlinear mixed-integer mathematical model, classified as NP-hard problems. We employ specially crafted metaheuristic algorithms as optimization strategies to address these challenges and boost the overall system performance.FindingsThe study introduces six newly designed metaheuristic algorithms. Solve the optimization problem. When comparing results between the traditional RRAP method and the innovative RRAP-CM method, enhanced reliability is achieved through the blending of diverse components. The use of metaheuristic algorithms proves advantageous in identifying optimal configurations, ensuring resource efficiency and maximizing energy output in a hybrid energy system.Research limitations/implicationsThe study’s findings have significant social implications because they contribute to the renewable energy field. The proposed methodologies offer a flexible and reliable mechanism for enhancing the efficiency of hybrid energy systems. By addressing the intermittent nature of renewable sources, this research promotes the design of highly reliable sustainable energy solutions, potentially influencing global efforts towards a more environmentally friendly and reliable energy landscape.Practical implicationsThe research provides practical insights by delivering a comprehensive analysis of a hybrid energy system incorporating both PV and wind components. Also, the use of metaheuristic algorithms aids in identifying optimal configurations, promoting resource efficiency and maximizing reliability. These practical insights contribute to advancing sustainable energy solutions and designing efficient, reliable hybrid energy systems.Originality/valueThis work is original as it combines the RRAP-CM methodology with six new robust metaheuristics, involving the integration of diverse components to enhance system reliability. The formulation of a nonlinear mixed-integer mathematical model adds complexity, categorizing it as an NP-hard problem. We have developed six new metaheuristic algorithms. Designed specifically for optimization in hybrid energy systems, this further highlights the uniqueness of this approach to research.

In industrial manufacturing systems, failures of machines caused by faults in their key components greatly influence operational safety and system reliability. Many data-driven methods have been developed for machinery diagnostics and prognostics. However, there lacks sufficient labeled data to train a high-performance data-driven model. Moreover, machinery datasets are usually collected from different operation conditions and mechanical components, leading to poor model generalization. To address these concerns, cross-domain transfer learning methods are applied to enhance the feasibility and accuracy of data-driven methods for machinery diagnostics and prognostics. This paper presents a comprehensive survey about how recent studies apply diverse transfer learning methods into machinery tasks including diagnostics and prognostics. Three types of commonly-used transfer methods, i.e., model and parameter transfer, feature matching and adversarial adaptation, are systematically summarized and elaborated on their main ideas, typical models and corresponding representative studies on machinery diagnostics and prognostics. In addition, ten widely-used open-source machinery datasets are presented. Based on recent research progress, this survey expounds emerging challenges and future research directions of transfer learning for industrial applications. This survey presents a systematic review of recent research with clear explanations as well as in-depth insights, thereby helping readers better understand transfer learning for machinery diagnostics and prognostics.

Short-term tracking is an open and challenging problem for which discriminative correlation filters (DCF) have shown excellent performance. We introduce the channel and spatial reliability concepts to DCF tracking and provide a learning algorithm for its efficient and seamless integration in the filter update and the tracking process. The spatial reliability map adjusts the filter support to the part of the object suitable for tracking. This both allows to enlarge the search region and improves tracking of non-rectangular objects. Reliability scores reflect channel-wise quality of the learned filters and are used as feature weighting coefficients in localization. Experimentally, with only two simple standard feature sets, HoGs and colornames, the novel CSR-DCF method—DCF with channel and spatial reliability—achieves state-of-the-art results on VOT 2016, VOT 2015 and OTB100. The CSR-DCF runs close to real-time on a CPU.

To improve the fastening effect of electronic components in mechatronic equipment, the tightening sequence and frequency have a significant impact on the structural performance of these components. This study identifies an optimal fastening method through experimental verification, although some measurement errors were noted; the method remains highly reliable. First, theoretical calculations and experimental analyses were conducted to determine the torque values under different conditions. Then, simulations were performed to study the impact on the stress of each screw. Finally, experimental validation of the simulation results was carried out, and a linear relationship (with a value of 0.82) between the measured and theoretical values was explored. Consequently, an optimized screw group fastening method is proposed, and the torque calculation process is simplified, thereby enhancing the reliability and lifespan of electronic components in mechatronic systems.

A large vertical CNC lathe is a highly automated precision machine that consists of several subsystems. The performance and status of these subsystems directly affect the reliability of the machine tool. In this paper, the improved FMECA method is used to calculate the hazard degree of each subsystem and statistically identify its main failure parts. Then the reliability evaluation indexes of the two subsystems that are most prone to failure are calculated, and the reliability evaluation of this large vertical CNC lathe is carried out according to the reliability indexes obtained. The results show that the reliability evaluation grade of the spindle system and automatic tool changer system of this large vertical CNC lathe is “very high”, and the reliability of this large vertical CNC lathe is also very high.

Additive manufacturing (AM) or 3D printing is growing rapidly in the manufacturing industry and has gained a lot of attention from various fields owing to its ability to fabricate parts with complex features. The reliability of the 3D printed parts has been the focus of the researchers to realize AM as an end-part production tool. Machine learning (ML) has been applied in various aspects of AM to improve the whole design and manufacturing workflow especially in the era of industry 4.0. In this review article, various types of ML techniques are first introduced. It is then followed by the discussion on their use in various aspects of AM such as design for 3D printing, material tuning, process optimization, in situ monitoring, cloud service, and cybersecurity. Potential applications in the biomedical, tissue engineering and building and construction will be highlighted. The challenges faced by ML in AM such as computational cost, standards for qualification and data acquisition techniques will also be discussed. In the authors’ perspective, in situ monitoring of AM processes will significantly benefit from the object detection ability of ML. As a large data set is crucial for ML, data sharing of AM would enable faster adoption of ML in AM. Standards for the shared data are needed to facilitate easy sharing of data. The use of ML in AM will become more mature and widely adopted as better data acquisition techniques and more powerful computer chips for ML are developed.

Bioelectronic devices and components made from soft, polymer-based and hybrid electronic materials form natural interfaces with the human body. Advances in the molecular design of stretchable dielectric, conducting and semiconducting polymers, as well as their composites with various metallic and inorganic nanoscale or microscale materials, have led to more unobtrusive and conformal interfaces with tissues and organs. Nonetheless, technical challenges associated with functional performance, stability and reliability of integrated soft bioelectronic systems still remain. This Review discusses recent progress in biomedical applications of soft organic and hybrid electronic materials, device components and integrated systems for addressing these challenges. We first discuss strategies for achieving soft and stretchable devices, highlighting molecular and materials design concepts for incorporating intrinsically stretchable functional materials. We next describe design strategies and considerations on wearable devices for on-skin sensing and prostheses. Moving beneath the skin, we discuss advances in implantable devices enabled by materials and integrated devices with tissue-like mechanical properties. Finally, we summarize strategies used to build standalone integrated systems and whole-body networks to integrate wearable and implantable bioelectronic devices with other essential components, including wireless communication units, power sources, interconnects and encapsulation.Soft bioelectronic devices are made from polymer-based and hybrid electronic materials that form natural interfaces with the human body. In this Review, the authors present recent developments in soft bioelectronic sensors and actuators, and discuss system-level integration for wearable and implantable medical applications.

Silicon solar cells are a mainstay of commercialized photovoltaics, and further improving the power conversion efficiency of large-area and flexible cells remains an important research objective 1 , 2 . Here we report a combined approach to improving the power conversion efficiency of silicon heterojunction solar cells, while at the same time rendering them flexible. We use low-damage continuous-plasma chemical vapour deposition to prevent epitaxy, self-restoring nanocrystalline sowing and vertical growth to develop doped contacts, and contact-free laser transfer printing to deposit low-shading grid lines. High-performance cells of various thicknesses (55–130 μm) are fabricated, with certified efficiencies of 26.06% (57 μm), 26.19% (74 μm), 26.50% (84 μm), 26.56% (106 μm) and 26.81% (125 μm). The wafer thinning not only lowers the weight and cost, but also facilitates the charge migration and separation. It is found that the 57-μm flexible and thin solar cell shows the highest power-to-weight ratio (1.9 W g −1 ) and open-circuit voltage (761 mV) compared to the thick ones. All of the solar cells characterized have an area of 274.4 cm 2 , and the cell components ensure reliability in potential-induced degradation and light-induced degradation ageing tests. This technological progress provides a practical basis for the commercialization of flexible, lightweight, low-cost and highly efficient solar cells, and the ability to bend or roll up crystalline silicon solar cells for travel is anticipated. A study reports a combination of processing, optimization and low-damage deposition methods for the production of silicon heterojunction solar cells exhibiting flexibility and high performance.

In this paper, the n-cascade with P(X

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