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Signal classifications have benefited from the successes of ML and DNN architectures. Cough classification techniques mainly extract features such as the Mel Frequency CepstralCoefficients for training. Most of these works also focus on obtaining information from single data modalities. However, multimodal analysis has been shown to aggregate useful information from different modalities thereby improving the internal capacity of ML models at data analysis. In this research, we propose a multimodal cough data classification approach with scalograms images obtained by decomposing cough signals using continuous wavelet transform and clinical information of subjects obtained from the COUGHVID dataset. Our result shows improved precision as compared to expert analysis.

Rolling element bearings are machine components used to allow circular movement and hence deliver forces between components of machines used in diverse areas of industry. The likelihood of failure has the propensity of increasing under prolonged operation and varying working conditions. Hence, the accurate fault severity categorization of bearings is vital in diagnosing faults that arise in rotating machinery. The variability and complexity of the recorded vibration signals pose a great hurdle to distinguishing unique characteristic fault features. In this paper, the efficacy and the leverage of a pre-trained convolutional neural network (CNN) is harnessed in the implementation of a robust fault classification model. In the absence of sufficient data, this method has a high-performance rate. Initially, a modified VGG16 architecture is used to extract discriminating features from new samples and serves as input to a classifier. The raw vibration data are strategically segmented and transformed into two representations which are trained separately and jointly. The proposed approach is carried out on bearing vibration data and shows high-performance results. In addition to successfully implementing a robust fault classification model, a prognostic framework is developed by constructing a health indicator (HI) under varying operating conditions for a given fault condition.

Knowing the state-of-health (SOH) of equipment, device or component is very essential for the secure and dependable operation of a system. Electrolytic capacitors are undoubtedly one of the essential components of power supply modules used in aerial and underwater vehicles, and every equipment requires a conversion of voltage from one level to another. This has encouraged research into the components of the power supply used in such systems of which electrolytic capacitor is of interest in this study. In this paper, we explore a new approach to implementing prognostics and health management (PHM) for electrolytic capacitors and propose a method of estimating the SOH leading to the prediction of the remaining useful life (RUL). This is accomplished by using a bidirectional long short-term memory (BLSTM) network to capture the degradation trends. We demonstrate the power and leverage that this method brings to bear in encoding time-domain dependencies in accurately estimating the SOH bereft of state models as employed in traditional methods. We validate the proposed approach using capacitor data recorded at different electrical over-stress accelerated aging conditions. The proposed method surpasses other existing methods in RUL prediction as indicated by the error and relative accuracy.

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