Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.
LoRa technology draws attention for its use in industrial monitoring and control systems in which each end device or task is required to send data periodically to a (cloud) server. Despite its provision of a stable link, it suffers from data loss by signal suppression and interference. A real-time LoRa protocol is proposed that uses a slot scheduling to remove collision and device or node grouping based on signal attenuation to deal with signal suppression. Based on the definition of a frame-slot structure, a logical slot indexing algorithm is devised to tag a logical index to each slot. The logical indices enable the easy allocation of slots to nodes such that if each node sends data in the allocated slots, it can satisfy time constraint. To handle external interference caused by other networks, the protocol uses a multiple listen-before-talk (mLBT) mechanism that allows channel detection multiple times within one slot. Our protocol is compared analytically and experimentally with other ones to show its superior throughput and reliability against signal suppression and interference. INDEX TERMS LoRa protocol, real-time, reliability, task scheduling, TDMA.
This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.
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