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This paper establishes a framework to analyze the state of grinding equipment by vibration signals of multi-sensors, which aims to solve the difficult problem of online quality monitoring in the grinding process. Firstly, the MSDP (modified symmetrized dot pattern) method is developed to transform time series into visualized images, which integrates multi-sensor data while involving more representative information for classification. Then, optimal parameters to generate MSDP images are selected based on the developed index to clearly distinguish different types of images while resembling each other of the same type, which improves the quality of training images for more effective feature extraction. Aiming at improving the ability of feature extraction, a multi-scale convolutional neural network (CNN) model is designed to classify the MSDP images representing different grinding processes, which utilizes different scales of kernels to acquire multi-scale fault information. Experiments are carried out on a real robotic grinding system to verify the performance of the fault classification framework for robotic grinding monitoring. The results show that the images generated by the selected optimal parameters along with the MSDP method are more conducive to classification, and the proposed multi-scale CNN achieves improved classification performance by involving multi-scale features.

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise.

Motor faults, especially mechanical faults, reflect eminently faint characteristic amplitudes in the stator current. In order to solve the issue of the motor current lacking effective and direct signal representation, this paper introduces a visual fault detection method for an induction motor based on zero-sequence current and an improved symmetric dot matrix pattern. Empirical mode decomposition (EMD) is used to eliminate the power frequency in the zero-sequence current derived from the original signal. A local symmetrized dot pattern (LSDP) method is proposed to solve the adaptive problem of classical symmetric lattice patterns with outliers. The LSDP approach maps the zero-sequence current to the ultimate coordinate and obtains a more intuitive two-dimensional image representation than the time–frequency image. Kernel density estimation (KDE) is used to complete the information about the density distribution of the image further to enhance the visual difference between the normal and fault samples. This method mines fault features in the current signals, which avoids the need to deploy additional sensors to collect vibration signals. The test results show that the fault detection accuracy of the LSDP can reach 96.85%, indicating that two-dimensional image representation can be effectively applied to current-based motor fault detection.

Deep learning based on vibration signal image representation has proven to be effective for the intelligent fault diagnosis of bearings. However, previous studies have focused primarily on dealing with single-channel vibration signal processing, which cannot guarantee the integrity of fault feature information. To obtain more abundant fault feature information, this paper proposes a multivariate vibration data image representation method, named the multivariate symmetrized dot pattern (M-SDP), by combining multivariate variational mode decomposition (MVMD) with symmetrized dot pattern (SDP). In M-SDP, the vibration signals of multiple sensors are simultaneously decomposed by MVMD to obtain the dominant subcomponents with physical meanings. Subsequently, the dominant subcomponents are mapped to different angles of the SDP image to generate the M-SDP image. Finally, the parameters of M-SDP are automatically determined based on the normalized cross-correlation coefficient (NCC) to maximize the difference between different bearing states. Moreover, to improve the diagnosis accuracy and model generalization performance, this paper introduces the local-to-global (LG) attention block and locally enhanced positional encoding (LePE) mechanism into a Swin Transformer to propose the LEG Transformer method. Then, a novel intelligent bearing fault diagnosis method based on M-SDP and the LEG Transformer is developed. The proposed method is validated with two experimental datasets and compared with some other methods. The experimental results indicate that the M-SDP method has improved diagnostic accuracy and stability compared with the original SDP, and the proposed LEG Transformer outperforms the typical Swin Transformer in recognition rate and convergence speed.

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The financial support from the “Ministerio de Ciencia e Innovación” (under Projects numbers TRA2007-62708, DPI2011-25419, ENE2017-89965-P and “Tecnologías ecológicas para el transporte Urbano, ecoTRANS” (CDTI) is also gratefully acknowledged. The financial support of the Instituto Universitario de Tecnología Industrial (IUTA), through projects SV-17-GIJON-1-07 and SV-18-GIJON-1-04 has also helped in the final stages of the present study.

To reduce production costs and defects in manufacturing, maintaining stability in production quality and timely prevention from equipment abnormalities have become essential. Usually, the quality of grinding processes is closely related to the health of the machinery and the grinding wheel. Real-time monitoring and assessment of the machine’s health can help to detect processing problems earlier and generate fewer defective products. In this study, accelerometers are installed in four axes on a commercial grinding machine to measure the temporal accelerations during processing of linear slide rails. The parallel accuracy of two points across the top surface of a standard slider block assembled on the produced rail was employed as the check for product quality. The abnormal causes included motor malfunction, grinding wheel defect, transmission belt deterioration, etc. The symmetrized dot pattern (SDP) was then adopted to convert the temporal acceleration signal into figurative expression for each plausible anomaly. From the SDPs of the measured accelerations which produced NG slide rail, the measurements from A and Y axes were more sensitive than their X and Z axes counterparts. The implementation of the proposed approach demonstrated an improved yield rate from 96.9% to 98.6% during a four-month study.

Rolling bearings are a vital component of mechanical equipment. It is crucial to implement rolling bearing fault diagnosis research to guarantee the stability of the long-term action of mechanical equipment. Conversion of rolling bearing vibration signals into images for fault diagnosis research has been a practical diagnostic approach. The current paper presents a rolling bearing fault diagnosis method using symmetrized dot pattern (SDP) images and a deep residual network with convolutional block attention module (CBAM-DRN). The rolling bearing vibration signal is first visualized and transformed into an SDP image with distinct fault characteristics. Then, CBAM-DRN is utilized to derive characteristics directly and detect faults from the input SDP images. In order to prevent conventional time-frequency images from being limited by their inherent flaws and avoid missing the fault features, the SDP technique is employed to convert vibration signals into images for visualization. DRN enables adequate extraction of rolling bearing fault characteristics and prevents training difficulties and gradient vanishing in deep level networks. CBAM assists the diagnostic model in concentrating on the image’s more distinctive parts and preventing the interference of non-featured parts. Finally, the method’s validity was tested with a composite fault dataset of motor bearings containing multiple loads and fault diameters. The experimental results reflect that the presented approach can attain a diagnostic precision of over 99% and good stability and generalization.

Abstract Background and Hypothesis Cognitive control deficits are prominent in individuals with psychotic psychopathology. Studies providing evidence for deficits in proactive control generally examine average performance and not variation across trials for individuals—potentially obscuring detection of essential contributors to cognitive control. Here, we leverage intertrial variability through drift-diffusion models (DDMs) aiming to identify key contributors to cognitive control deficits in psychosis. Study Design People with psychosis (PwP; N = 122), their first-degree biological relatives (N = 78), and controls (N = 50) each completed 120 trials of the dot pattern expectancy (DPX) cognitive control task. We fit full hierarchical DDMs to response and reaction time (RT) data for individual trials and then used classification models to compare the DDM parameters with conventional measures of proactive and reactive control. Study Results PwP demonstrated slower drift rates on proactive control trials suggesting less efficient use of cue information. Both PwP and relatives showed protracted nondecision times to infrequent trial sequences suggesting slowed perceptual processing. Classification analyses indicated that DDM parameters differentiated between the groups better than conventional measures and identified drift rates during proactive control, nondecision time during reactive control, and cue bias as most important. DDM parameters were associated with real-world functioning and schizotypal traits. Conclusions Modeling of trial-level data revealed that slow evidence accumulation and longer preparatory periods are the strongest contributors to cognitive control deficits in psychotic psychopathology. This pattern of atypical responding during the DPX is consistent with shallow basins in attractor dynamic models that reflect difficulties in maintaining state representations, possibly mediated by excess neural excitation or poor connectivity.

This paper proposes a hybrid algorithm combining the symmetrized dot pattern (SDP) method and a convolutional neural network (CNN) for fault detection in lithium battery modules. The study focuses on four fault types: overcharge, over-discharge, aging, and leakage caused by manual perforation. An 80.5 kHz high-frequency square wave signal is input into the battery module and recorded using a high-speed data acquisition card. The signal is processed by the SDP method to generate characteristic images for fault diagnosis. Finally, a deep learning algorithm is used to evaluate the state of the lithium battery. A total of 3000 samples were collected, with 400 samples used for training and 200 for testing for each fault type, achieving an overall identification accuracy of 99.9%, demonstrating the effectiveness of the proposed method.

To address the issue of low recognition accuracy caused by incomplete information, a CNN-based fault diagnosis method for rolling bearings using multi-source resonance sparse component feature fusion (RSSD-P) is proposed in this paper, which effectively resolves the problem of impact features being masked. In noise-contaminated environments, bearing vibration signals exhibit nonstationarity, obscuring fault characteristics. To overcome this, resonance sparse decomposition was employed to extract impact-related fault features. Furthermore, to fully utilize multi-sensor information and enhance fault representation, a symmetric dot pattern (SDP) method was introduced to fuse multi-source fault impact features, achieving effective integration of impact characteristics from multi-source vibration signals. A CNN-based approach incorporating multi-source resonance sparse component and SDP feature fusion was developed, and a bearing fault diagnosis model was established accordingly. Experimental results demonstrate that the proposed method achieves a fault recognition accuracy of 98.63% under varying operating conditions. Compared with other bearing fault diagnosis methods, the recognition precision is improved by 8.49%~17.8%, confirming its superior performance.