FEMSN: Frequency-Enhanced Multiscale Network for fault diagnosis of rotating machinery under strong noise environments

Rolling bearings are critical components of rotating machinery, and their proper functioning is essential for industrial production. Most existing condition monitoring methods focus on extracting discriminative features from time-domain signals to assess bearing health status. However, under complex operating conditions, periodic impulsive characteristics related to fault information are often obscured by noise interference. Consequently, existing approaches struggle to learn distinctive fault-related features in such scenarios. To address this issue, this paper proposes a novel CNN-based model named FEMSN. Specifically, a Fourier Adaptive Denoising Encoder Layer (FADEL) is introduced as an input denoising layer to enhance key features while filtering out irrelevant information. Subsequently, a Multiscale Time-Frequency Fusion (MSTFF) module is employed to extract fused time-frequency features, further improving the model robustness and nonlinear representation capability. Additionally, a distillation layer is incorporated to expand the receptive field. Based on these advancements, a novel deep lightweight CNN model, termed the Frequency-Enhanced Multiscale Network (FEMSN), is developed. The effectiveness of FEMSN and FADEL in machine health monitoring and stability assessment is validated through two case studies.

Machine Learning for Automated Mitral Regurgitation Detection from Cardiac Imaging

Mitral regurgitation (MR) is a heart valve disease with potentially fatal consequences that can only be forestalled through timely diagnosis and treatment. Traditional diagnosis methods are expensive, labor-intensive and require clinical expertise, posing a barrier to screening for MR. To overcome this impediment, we propose a new semi-supervised model for MR classification called CUSSP. CUSSP operates on cardiac imaging slices of the 4-chamber view of the heart. It uses standard computer vision techniques and contrastive models to learn from large amounts of unlabeled data, in conjunction with specialized classifiers to establish the first ever automated MR classification system. Evaluated on a test set of 179 labeled -- 154 non-MR and 25 MR -- sequences, CUSSP attains an F1 score of 0.69 and a ROC-AUC score of 0.88, setting the first benchmark result for this new task.

Multi-Resolution Weak Supervision for Sequential Data

Since manually labeling training data is slow and expensive, recent industrial and scientific research efforts have turned to weaker or noisier forms of supervision sources. However, existing weak supervision approaches fail to model multi-resolution sources for sequential data, like video, that can assign labels to individual elements or collections of elements in a sequence. A key challenge in weak supervision is estimating the unknown accuracies and correlations of these sources without using labeled data. Multi-resolution sources exacerbate this challenge due to complex correlations and sample complexity that scales in the length of the sequence. We propose Dugong, the first framework to model multi-resolution weak supervision sources with complex correlations to assign probabilistic labels to training data. Theoretically, we prove that Dugong, under mild conditions, can uniquely recover the unobserved accuracy and correlation parameters and use parameter sharing to improve sample complexity. Our method assigns clinician-validated labels to population-scale biomedical video repositories, helping outperform traditional supervision by 36.8 F1 points and addressing a key use case where machine learning has been severely limited by the lack of expert labeled data. On average, Dugong improves over traditional supervision by 16.0 F1 points and existing weak supervision approaches by 24.2 F1 points across several video and sensor classification tasks.