Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological process (falling pieces of rock) genera...
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Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological process (falling pieces of rock) generates random impulses that complicate detection. To address this, signal pre-filtering (extracting the informative frequency band from noise-affected signals) is necessary. This paper proposes an algorithm for processing vibration signals from a bearing used in an ore crusher. Selecting informative frequency bands (IFBs) in the presence of impulsive noise is notably challenging. The approach employs correlation maps to detect cyclic behavior within specific frequency bands in the time-frequency domain (spectrogram), enabling the identification of IFBs. Robust correlation measures and median filtering are applied to enhance the correlation maps and improve the final IFB selection. Signal segmentation and the use of averaged results for IFB selection are also highlighted. The proposed trimmed and quadrant correlations are compared with the Pearson and Kendall correlations using simulated signal, real vibration signal from crusher in mining industry and acoustic signal measured on the test rig. Furthermore, the results of real vibration analyses are compared with established IFB selectors, including the spectral kurtosis, the alpha selector and the conditional variance-based selector.
Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient faultdetection for industrial processes is a big challenge because of the faint fault signature. To improve the performan...
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Modern industrial processes often exhibit large-scale and nonlinear characteristics. Incipient faultdetection for industrial processes is a big challenge because of the faint fault signature. To improve the performance of incipient faultdetection for large-scale nonlinear industrial processes, a decentralized adaptively weighted stacked autoencoder (DAWSAE) -based faultdetection method is proposed. First, the industrial process is divided into several sub-blocks and local adaptively weighted stacked autoencoder (AWSAE) is established for each sub-block to mine local information and obtain local adaptively weighted feature vectors and residual vectors. Second, the global AWSAE is established for the whole process to mine global information and obtain global adaptively weighted feature vectors and residual vectors. Finally, local statistics and global statistics are constructed based on local and global adaptively weighted feature vectors and residual vectors to detect the sub-blocks and the whole process, respectively. The advantages of proposed method are verified by a numerical example and Tennessee Eastman process (TEP).
This paper demonstrates the usefulness of distributed local verification of proofs, as a tool for the design of self-stabilizing algorithms. In particular, it introduces a somewhat generalized notion of distributed lo...
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This paper demonstrates the usefulness of distributed local verification of proofs, as a tool for the design of self-stabilizing algorithms. In particular, it introduces a somewhat generalized notion of distributed local proofs, and utilizes it for improving the time complexity significantly, while maintaining space optimality. As a result, we show that optimizing the memory size carries at most a small cost in terms of time, in the context of minimum spanning tree (MST). That is, we present algorithms that are both time and space efficient for both constructing an MST and for verifying it. This involves several parts that may be considered contributions in themselves. First, we generalize the notion of local proofs, trading off the time complexity for memory efficiency. This adds a dimension to the study of distributed local proofs, which has been gaining attention recently. Specifically, we design a (self-stabilizing) proof labeling scheme which is memory optimal (i.e., bits per node), and whose time complexity is in synchronous networks, or time in asynchronous ones, where is the maximum degree of nodes. This answers an open problem posed by Awerbuch et al. (1991). We also show that time is necessary, even in synchronous networks. Another property is that if faults occurred, then, within the required detection time above, they are detected by some node in the locality of each of the faults. Second, we show how to enhance a known transformer that makes input/output algorithms self-stabilizing. It now takes as input an efficient construction algorithm and an efficient self-stabilizing proof labeling scheme, and produces an efficient self-stabilizing algorithm. When used for MST, the transformer produces a memory optimal self-stabilizing algorithm, whose time complexity, namely, , is significantly better even than that of previous algorithms (the time complexity of previous MST algorithms that used memory bits per node was , and the time for optimal space algorithms
The extraction of the informative frequency band from the signal with heavy-tailed noise is introduced to detect local damage. The algorithm for the vibration signal from a bearing installed in the ore crusher is prop...
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The extraction of the informative frequency band from the signal with heavy-tailed noise is introduced to detect local damage. The algorithm for the vibration signal from a bearing installed in the ore crusher is proposed. From a signal processing point of view, this is related to the detection and recognition of cyclic and non-cyclic impulsive components in the signal. It is assumed that the non-periodic impulses have amplitudes significantly higher than the cyclic impulses - usually fully hidden in the non-Gaussian noise - which makes the diagnosis difficult. As both components are non-Gaussian, a periodicity detector can be used as an alternative to the impulsiveness criteria. In this paper, the proposed informative frequency band selectors utilize the dependency measures, i.e., the Pearson, Spearman and Kendall correlations. These dependency measures are applied to the time-frequency domain representation of the signal to find similarities between sub-signals associated with frequency bands. Three types of frequency bands are expected - the first one - with just Gaussian noise, the second one - with cyclic impulsive behavior, and the last one - with non-cyclic impulsive behavior. For simplicity, it is assumed that frequency bands occupied by cyclic and non-cyclic impulses are different. Finally, one may obtain a map of similarities between all frequency bands, and based on this map the 1D selector for informative frequency band identification can be estimated. The main contribution of the paper is the introduction of the holistic procedure of the fault diagnosis containing the time-frequency representation of vibration signal, including the estimation of the dependency map (D-map) using three different dependency measures, D-map de-noising, and the integration of D-map to one-dimensional (1D) selector. The introduced procedure is applied to the complex simulated data and then to real vibrations from the heavy-duty machine. (C) 2021 Elsevier Ltd. All rights reser
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