This paper studies the remaining useful life (RUL) of lubricating oil based on condition monitoring (CM). Firstly, the element composition and content of the lubricating oil in use were quantitatively analyzed by atomic emission spectrometry (AES). Considering the large variety of oil data obtained through AES, the accuracy and efficiency of the RUL prediction model may be reduced. To solve this problem, a comprehensive parameter selection method based on information entropy, correlation analysis, and lubricant deterioration analysis is proposed to screen oil data. Then, based on a support vector machine (SVM), the RUL prediction model of lubricant was established. By comparing the experimental results with the output data of the prediction model, it is shown that the accuracy and efficiency of the SVM prediction model established after parameter screening have been significantly improved.

In order to solve the problem that the accuracy is not high when the traditional fuzzy morphological associative memories network (FMAM) is used to classify samples, based on quantum superposition and collapse, the quantum fuzzy morphological associative memories network (QFMAM) is proposed. In QFMAM, the structural element is constructed by the qubit system and the qubit probability represents corresponding membership degree to obtain adaptive structural elements. The samples are preprocessed first and then classified to ensure high classification accuracy. QFMAM, FMAM, support vector machine (SVM) and naive Bayes classifier (NBC) are used to classify simulation data and experimental signals of rolling bearings respectively. Comparing the performance of the four classifiers, it is obvious that the training time of QFMAM is far less than SVM and NBC, and the classification accuracy of the test samples by QFMAM is higher than that by the other three classifiers. QFMAM is very suitable for intelligent diagnosis of rolling bearing fault.

Road-object detection for autonomous driving faces challenges including dense occlusions, large-scale variations, and imaging degradation under nighttime and adverse weather conditions, making it difficult for efficient detectors to balance accuracy and robustness. To address this, we propose EDP-Net, an efficient and robust detection network tailored for autonomous driving. First, we design a Learnable Selective Edge Enhancement Module (LSEM) with channel recalibration to suppress background interference while strengthening discriminative edge cues. Second, we construct a Dual-Path Soft-Sparse Feature Pyramid Network (DSFPN), introducing a detail-compensation path to enhance representations of distant small objects. We further develop a deep Global-Local Soft-Sparse Attention Module (GLSAM) to establish long-range context with low computational overhead and incorporate DySample to mitigate cross-scale alignment errors. Finally, we propose a Dynamic Task-Aligned Decoupled Head (DTADH), which employs dynamic task routing and branch-specific alignment to construct task-adaptive features for classification and localization under occlusion and inter-class similarity. Experiments show that EDP-Net achieves 67.0% AP50 and 43.9% AP on BDD-100K. Under a retraining-based evaluation on KITTI, EDP-Net obtains 93.8% AP50 and 74.5% AP, indicating that the proposed architecture remains effective on another autonomous-driving dataset.