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Wheels are vital components on railway vehicles and may encounter various problems during long-term operation, e.g., wear and defects. The existing preventative reprofiling strategy with fixed reprofiling interval and cutting depth causes huge maintenance costs. In order to reduce life cycle cost (LCC), this paper proposes an optimal life cycle reprofiling (LCR) strategy for risk-based preventative maintenance of train wheels. First, the probabilistic models of wheel wear, reprofiling, and defects are built. Then, Markov decision process is constructed to model a complete wheel degradation process in its life cycle with sequential decision-making of reprofiling. Meanwhile, the reward matrices are designed considering LCC and safety-related costs. Further, a two-loop optimization scheme is proposed to pursue the optimal LCR strategy minimizing LCC. In the inner loop, policy iteration is adopted to pursue the optimal reprofiling policy. In the outer loop, gradient descent with momentum is employed to optimize the overhaul limits. This scheme achieves fast convergence and global optimization of maintenance decision-making for both routine and overhaul reprofiling. Finally, the mechanism of optimal reprofiling policy is exemplified, including the principles of prolonging, protecting, and abandoning. The main trade-offs in cutting depth and overhaul limits provide the feasibility for optimization. A case study and field tests demonstrate that through controlling wheel degradation and life span, LCR strategy can not only reduce the LCC of wheel maintenance, but also improve the reliability and safety of wheel operation. [ABSTRACT FROM AUTHOR]

With regard to the state-of-the-art technologies in the fields of applied superconductivity and electromagnetic devices, research and development highlights are presented. The recent progress and achievement described with principle and technical details include mainly i) applied superconducting materials; ii) superconducting magnets and their applications such as in ITER and Tokamaks; iii) high Tc superconducting (HTS) magnetic levitation and applications; iv) HTS smart grids; v) superconducting and electromagnetic material modelling and characterization; and vi) advanced electromagnetic devices. The applied superconductivity technology and availability are especially focused and verified with the trend of development prospection. [ABSTRACT FROM AUTHOR]

High temperature superconducting (HTS) magnetic levitation (maglev) has the potential as a high-speed passenger transportation mode owing to its passive stabilization. Our group is constructing a 1500 m-long HTS maglev high speed test line. As a kind of passenger transport, safety and comfort are primary factors for its operation quality. However, the weak damping in HTS maglev system is not enough to suppress the vibration. As a preliminary study of the test line, this article investigates an additional suspension system aiming to improve the dynamic characteristics of HTS maglev through experiments and simulation. We first built an HTS maglev model vehicle composed of a levitation frame and a car body. The dynamic responses were tested based on a dynamic measurement system. Second, a dynamic model of this kind of maglev device was established and the influence of suspension parameters on its vibration performance is analyzed theoretically. Third, this suspension system was applied to an HTS maglev engineering prototype through numerical simulations. Experimental and simulation results manifested that the suspension system can effectively reduce the vibration, especially with high frequencies. This article provides a reference for the design of HTS maglev vehicles. [ABSTRACT FROM AUTHOR]

Non-volatile Random Access Memories (NVRAM) have emerged in recent years to bridge the performance gap between the main memory and external storage devices, such as Solid State Drives (SSD). In addition to higher storage density, NVRAM provides byte-addressability, higher bandwidth, near-DRAM latency, and easier access compared to block devices such as traditional SSDs. This enables new programming paradigms taking advantage of durability and larger memory footprint. With the range and size of GPU workloads expanding, NVRAM will present itself as a promising addition to GPU's memory hierarchy. To utilize the non-volatility of NVRAMs, programs should allow durable stores, maintaining consistency through a power loss event. This is usually done through a logging mechanism that works in tandem with a transaction execution layer which can consist of a transactional memory or a locking mechanism. Together, this results in a transaction processing system that preserves the ACID properties. GPUs are designed with high throughput in mind, leveraging high degrees of parallelism. Transactional memory proposals enable fine-grained transactions at the GPU thread-level. However, with lower write bandwidths compared to that of DRAMs, using NVRAM as-is may yield sub-optimal overall system performance when threads experience long latency. To address this problem, we propose using Helper Warps to move persistence out of the critical path of transaction execution, alleviating the impact of latencies. Our mechanism achieves a speedup of 4.4 and 1.5 under bandwidth limits of 1.6 GB/s and 12 GB/s and is projected to maintain speed advantage even when NVRAM bandwidth gets as high as hundreds of GB/s in certain cases. Due to the speedup, our proposed method also results in reduction in overall energy consumption. [ABSTRACT FROM AUTHOR]

B+tree is one of the most important data structures and has been widely used in different fields. With the increase of concurrent queries and data-scale in storage, designing an efficient B+tree structure has become critical. Due to abundant computation resources, SIMD architectures provide potential opportunities to achieve high query throughput for B+tree. However, prior methods cannot achieve satisfactory performance results due to low resource utilization and poor memory performance. In this paper, we first identify the gaps between B+tree and SIMD architectures. Concurrent B+tree queries involve many global memory accesses and different divergences, which mismatch with SIMD architecture features. Based on this observation, we propose Harmonia, a novel B+tree structure to bridge the gaps. In Harmonia, a B+tree structure is divided into a key region and a child region. The key region stores the nodes with its keys in a breadth-first order. The child region is organized as a prefix-sum array, which only stores each node's first child index in the key region. Since the prefix-sum child region is small and the children's index can be retrieved through index computations, most of it can be stored in on-chip caches, which can achieve good cache locality. To make it more efficient, Harmonia also includes two optimizations: partially-sorted aggregation and narrowed thread-group traversal, which can mitigate memory and execution divergence and improve resource utilization. Evaluations on a 28-core INTEL CPU show that Harmonia can achieve up to 207 million queries per second, which is about 1.7X faster than that of CPU-based HB+Tree , a recent state-of-the-art solution. And on a Volta TITAN V GPU, it can achieve up to 3.6 billion queries per second, which is about 3.4X faster than that of GPU-based HB+Tree. [ABSTRACT FROM AUTHOR]

Extracting fault-related features from noisy vibration signals is a pivotal prerequisite for condition monitoring of rolling element bearings. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a non-iterative blind deconvolution algorithm and has been proven as an efficient tool for periodic impulses extraction of rotating machinery. However, the effectiveness of MOMEDA strongly relies on the accuracy of the prior impulse period. Additionally, the MOMEDA method suffers from serious edge effect, resulting in the filtered signal shorter than the original signal. To solve these limitations of MOMEDA, a novel deconvolution algorithm called adaptive MOMEDA (AMOMEDA) is presented in this paper. The new method adopts a periodic modulation intensity (PMI) based strategy to automatically estimate the impulse period rather than a fixed prior period. The impulse period can gradually approximate the real fault-related period with the update of the filtered signal after every iterative step. To overcome the edge effect of MOMEDA, a waveform extension strategy is designed to adaptively restore the length of the filtered signal as the original signal according to the local characteristics of the signal at the left boundary. Simulated and experimental results of railway axle-box bearing demonstrated the effectiveness and superiority of AMOMEDA by comparison with MOMEDA. Moreover, the proposed method is compared with fast kurtogram and the improved complete ensemble empirical mode decomposition with adaptive noise to show that the proposed method is more effective. [ABSTRACT FROM AUTHOR]

The Axle-box bearing (ABB) is a key component in the high-speed train which suffers multiple stochastic excitations in realistic working conditions. The ABB's health status is crucial for the reliable and safe operation of the rolling stock. This paper proposes a novel stochastic vehicle–track coupled model to evaluate the dynamic performance of ABBs in a high-speed train with unsteady wind load and random track irregularities. The nonlinear bearing stiffness and unsmooth nonlinearities of the bearing clearances are properly considered based on the Hertzian contact theory. The von Karman power spectral density is adopted to generate the time-history of the fluctuating wind speed on a moving vehicle. The unsteady wind loads acting on the carbody are derived based on the fluid-induced vibration theory. The empirical PSD of track irregularity is adopted to generate the time-history of track irregularities. The Monte Carlo method is employed to analyse the roller–raceway contact stress with random excitations of unsteady wind and track irregularities. The results indicate that the crosswind causes the partial loading phenomenon of the ABBs. The increase of crosswind speed significantly increases the probability of the contact stress exceeding the safety threshold, which challenges the service safety of the key components inside the train. [ABSTRACT FROM AUTHOR]