As a single-task classification method, support matrix machine (SMM) is widely used in mechanical equipment fault diagnosis. However, when diagnosing multi-objective tasks, it is difficult to fully utilize the common information between multiple tasks, resulting in limited information contained in the model. Meanwhile, SMM is extremely sensitive to abnormal samples, which is not conducive to model construction. To address the aforementioned issues, a novel multiple restriction cross-border matrix machine (MRCBMM) is proposed. In MRCBMM, a weighted constraint group (WCG) is firstly designed to adjust the influence of different abnormal samples on the hyperplane, thereby determining the optimal position of the hyperplane. Meanwhile, MRCBMM defines a matrix kernel expansion (MKE) that maps matrix samples to high-dimensional space to fully utilize the structural information of the original signal. In addition, to achieve cross-border diagnosis of MRCBMM, a regularized multi-task learning framework is constructed to complete the features and parameters. Two sets of multi-objective rotating mechanical fault datasets are used for validation, and the results show that MRCBMM improves diagnostic accuracy by approximately 2% in scenarios containing abnormal samples compared with existing methods, and consistently achieves over 98% accuracy on clean datasets.

Mechanical fault diagnosis faces significant challenges due to the susceptibility of traditional matrix classifiers to abnormal samples and their inability to leverage cross domain information. To address these limitations, this paper introduces a novel fuzzy cross domain matrix machine (FCDMM), which integrates fuzzy membership modeling with multi-task learning to enhance robustness and enable knowledge transfer across related tasks. FCDMM constructs fuzzy-based decision hyperplanes and designs adaptive boundary factors to mitigate the influence of outliers. Extensive experiments on multiple domain fault datasets demonstrate that FCDMM achieves remarkable accuracy rates of 98.86 % for bearing faults and 99.57 % for gear faults, significantly outperforming state-of-the-art methods.

The development of spectral analysis methods is very rapid, but these methods rarely take into account the difference of feature extraction under strong and weak random noise. In this article, a new adaptive spectral analysis method called enhanced Ramanujan spectrum (ERS) is proposed to strengthen the ability of feature extraction and noise robustness. First, hybrid Ramanujan Fourier transform is used to improve the calculation accuracy and period recognition ability of discrete Fourier transform. Second, generalized Ramanujan spectrum (GRS) is used to obtain features in the frequency domain. Finally, the ERS can be adaptively constructed by the optimal GRSs in each segment to reduce the influence of random noise. The analysis results of rolling bearing fault signals show that ERS is an effective feature extraction method and can be used in fault diagnosis field.

Support matrix machine (SMM) based methods have revolutionized the field of state identification by effectively mining correlations between fault features. However, some flaws limit its ability to handle interfered and limited samples, deriving from the purely focus on the closer samples nearing classify boundary and the thin design of binary classification nature, thus resulting SMM ignores the correlations between different samples and cannot align with the reality on the limited multiclass fault data. To address this issue, a novel approach called multiclass graph embedding support matrix machine (MGESMM) is proposed in this article. First, similarity matrix composed of similarity coefficient between each two samples are calculated by cosine distance. This similarity matrix is then used in manifold regularization-based graph embedding model, which can eliminate the negative impact of interfered and limited samples. Second, hamming loss-based predict error evaluation and multiclass loss-based boundary constraint is designed to form a direct multiclass classification constraint, thus the drawbacks of one-versus-one or one-versus-rest strategies for multiclass classification are prevented. Finally, to evaluate the efficacy of MGESMM, two roller bearing damage identification experiments are analyzed, and the results demonstrate that MGESMM achieves superior performance under different operating conditions.

Abstract Most transfer learning-based fault diagnosis methods learn diagnostic information from the source domain to enhance performance in the target domain. However, in practical applications, usually there are multiple available source domains, and relying on diagnostic information from only a single source domain limits the transfer performance. To this end, a non-uniformly weighted multisource domain adaptation network is proposed to address the above challenge. In the proposed method, an intra-domain distribution alignment strategy is designed to eliminate multi-domain shifts and align each pair of source and target domains. Furthermore, a non-uniform weighting scheme is proposed for measuring the importance of different sources based on the similarity between the source and target domains. On this basis, a weighted multisource domain adversarial framework is designed to enhance multisource domain adaptation performance. Numerous experimental results on three datasets validate the effectiveness and superiority of the proposed method.

In this article, a new deconvolution method, named maximum Ramanujan spectrum signal-to-noise ratio deconvolution (MRSD) method is proposed. MRSD updates the filter by maximizing the index ofv-Ramanujan spectrum signal-to-noise ratio (v-RSSNR) to improve the noise reduction effect and the performance of feature enhancement. On the one hand, the concept of generalized envelope is introduced into the MRSD method, and flexible envelopes are used to enhance the weak state features, and thev-Ramanujan spectrum of the signal is analyzed by using the mixed Ramanujan Fourier transform, so as to provide an optimal plane for the evaluation of weak state features. On the other hand, the MRSD method designs the filter by maximizing thev-RSSNR index, and optimizes the objective function by gradient descent. The simulation and experimental analysis results show that MRSD method is an effective noise reduction method and can accurately extract weak state features.

Exploring historical measurement data-driven health monitoring schemes for roller bearings is a current research hotspot. In engineering practice, the type of fault data obtained is often unknown and requires expensive costs to be annotated. However, most current intelligent diagnostic methods are based on the assumption that the labeled fault data is sufficient, so as to effectively establish the nonlinear mapping relationship between monitoring signals and health status. For this issue, a newly intelligent diagnosis method based on semi-supervised matrixized graph embedding machine (SMGEM) is proposed. In SMGEM, the geometric similarity relationship of unlabeled and labeled samples is obtained, which is subsequently embedded by incorporating a manifold regularization into the SMGEM model, so that SMGEM can use the structure information of unlabeled samples to assist modeling. Meanwhile, a weighted nuclear norm is used to highlight the importance of large singular values, so that a more accurate weight matrix can be constructed. The proposed method is verified by several roller bearing fault datasets, and experimental results demonstrate that the proposed semi-supervised diagnosis method can use a few labeled samples to obtain a better identification accuracy.

As an important part of rotating machinery, gear is easy to appear some unexpected fault states, and its fault diagnosis is very important. Fourier decomposition method (FDM) is a common method for gear fault diagnosis, but the noise robustness, period recognition, and extraction capabilities of FDM are unsatisfactory. Based on this, in this article, Ramanujan Fourier mode decomposition (RFMD) method is proposed. The RFMD not only has a complete mathematical theory foundation but also has an excellent ability to identify and extract periodic components. Emulational and experimental results of planetary gearbox show that the RFMD method has good noise robustness and can accurately extract gear fault characteristic information. Thus, it is an effective gear fault diagnosis method.

As an emerging technique, non-volatile memory (NVM) provides valuable opportunities for boosting the memory system, which is vital for the computing system performance. However, one challenge preventing NVM from replacing DRAM as the main memory is that NVM row activation's latency is much longer (by approximately 10x) than that of DRAM. To address this issue, we present a collective cross-page prefetching scheme that can accurately open an NVM row in advance and then prefetch the data blocks from the opened row with low overhead. We identify a memory access pattern (referred to as a ladder stream) to facilitate prefetching that can cross page boundary, and propose the ladder stream prefetcher (LSP) for NVM. In LSP, two crucial components have been well designed. Collective Prefetch Table is proposed to reduce the interference with demand requests caused by prefetching through speculatively scheduling the prefetching according to the states of the memory queue. It is implemented with low overhead by using single entry to track multiple prefetches. Memory Mapping Table is proposed to accurately prefetch future pages by maintaining the mapping between physical and virtual addresses. Experimental evaluations show that LSP improves the memory system performance with no prefetching by 66%, and the improvement over the state-of-the-art prefetchers, Access Map Pattern Matching Prefetcher (AMPM), Best-Offset Prefetcher (BOP) and Signature Path Prefetcher (SPP) is 26.6%. 21.7% and 27.4%. respectively.

Emerging Non-Volatile Memory (NVM) provides both larger memory capacity and higher energy efficiency, but has much longer access latency than traditional DRAM, thus DRAM can be used as an efficient cache to hide the long latency of Non-Volatile Main Memory (NVMM) system. Transparent Off-chip DRAM cache (TOD cache) is a new DRAM cache structure where off-chip DRAM module is used as L4 cache and managed by hardware. The capacity and latency ratio of TOD cache over NVM are both quite different from those of traditional on-chip SRAM or die-stacked DRAM cache over off-chip DRAM memory. All the factors including hit latency, miss latency and hit rate need to be re-considered for TOD cache design. In this study, we first point out that three types of traditional cache schemes cannot be used directly for TOD cache, since set-associative cache suffers from extra tag lookup latency, direct-mapped cache has low hit rate and tag cache is too small to efficiently hold the working sets of tags for DRAM cache. Based on these observations, we propose a novel cache scheme, TDV, that fuses these three different types of cache together to take their advantages. In TDV, a direct-mapped cache is used as the first-level cache to achieve short access latency, a set-associative victim cache is taken as the second-level cache to obtain extra high hit rate, and a SRAM tag cache only serves for the victim cache rather than the whole DRAM cache and thus improves the hit rate of tag cache significantly. The simulation results show that, TDV cache has a performance improvement of 6.3% and 8.3% on average than state-of-the-art direct-mapped (Alloy cache) and set-associative cache (ATCache) with same DRAM and SRAM capacity.

Limited main memory size is considered as one of the major bottlenecks in virtualization environments. Content-Based Page Sharing (CBPS) is an efficient memory deduplication technique to reduce server memory requirements, in which pages with same content are detected and shared into a single copy. As the widely used implementation of CBPS, Kernel Samepage Merging (KSM) maintains the whole memory pages into two global comparison trees (a stable tree and an unstable tree). To detect page sharing opportunities, each tracked page needs to be compared with pages already in these two large global trees. However since the vast majority of compared pages have different content with it, that will induce massive futility comparisons and thus heavy overhead.