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Journal ArticleDOI

Advanced bearing diagnostics: A comparative study of two powerful approaches

TL;DR: This paper investigates and compares two emerging approaches to vibration-based fault detection based on a cyclostationary modeling of the bearing signal and addresses the extension of these approaches to the nonstationary operating regime.
About: This article is published in Mechanical Systems and Signal Processing.The article was published on 2019-01-01. It has received 101 citations till now. The article focuses on the topics: Fault detection and isolation & Cyclostationary process.
Citations
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Journal ArticleDOI
TL;DR: An overview of the Deep Digital Twin framework is presented and it is demonstrated that the DDT is able to detect incipient faults, track asset degradation and differentiate between failure modes in both stationary and non-stationary operating conditions when trained on only healthy operating data.

122 citations

Journal ArticleDOI
01 Jul 2020
TL;DR: The experimental results demonstrate that the proposed approach correctly identifies single and recurrent anomalies without any prior knowledge of their characteristics, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster.
Abstract: Subsequence anomaly detection in long sequences is an important problem with applications in a wide range of domains. However, the approaches that have been proposed so far in the literature have severe limitations: they either require prior domain knowledge that is used to design the anomaly discovery algorithms, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose an unsupervised method suitable for domain agnostic subsequence anomaly detection. Our method, Series2Graph, is based on a graph representation of a novel low-dimensionality embedding of subsequences. Series2Graph needs neither labeled instances (like supervised techniques), nor anomaly-free data (like zero-positive learning techniques), and identifies anomalies of varying lengths. The experimental results, on the largest set of synthetic and real datasets used to date, demonstrate that the proposed approach correctly identifies single and recurrent anomalies without any prior knowledge of their characteristics, outperforming by a large margin several competing approaches in accuracy, while being up to orders of magnitude faster.

76 citations


Cites background from "Advanced bearing diagnostics: A com..."

  • ...[Limitations of Previous Approaches] Some existing techniques explicitly look for a set of pre-determined types of anomalies [27, 2]....

    [...]

Journal ArticleDOI
TL;DR: A new transfer learning method based on bidirectional Gated Recurrent Unit (TBiGRU) is proposed to accurately predict the RUL of bearings under different working conditions and can adaptively recognize different running states of bearings and obtain corresponding training labels, and realize better RUL prediction performance under differentWorking conditions.

74 citations

Journal ArticleDOI
TL;DR: In this article, a physics-informed deep learning approach was proposed for bearing condition monitoring and fault detection, which consists of a simple threshold model and a deep convolutional neural network (CNN) model.

56 citations

Proceedings ArticleDOI
20 Apr 2020
TL;DR: NorM is a novel approach, suitable for domain-agnostic anomaly detection, based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior.
Abstract: Subsequence anomaly (or outlier) detection in long sequences is an important problem with applications in a wide range of domains. However, current approaches have severe limitations: they either require prior domain knowledge, or become cumbersome and expensive to use in situations with recurrent anomalies of the same type. In this work, we address these problems, and propose NorM, a novel approach, suitable for domain-agnostic anomaly detection. NorM is based on a new data series primitive, which permits to detect anomalies based on their (dis)similarity to a model that represents normal behavior. The experimental results on several real datasets demonstrate that the proposed approach outperforms by a large margin the current state-of-the art algorithms in terms of accuracy, while being orders of magnitude faster.

53 citations


Cites background from "Advanced bearing diagnostics: A com..."

  • ...Existing techniques either explicitly look for a set of pre-determined types of anomalies [6], [7], or identify as anomalies the subsequences with the largest distances to their nearest neighbors (termed discords) [5], [8]....

    [...]

References
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Journal ArticleDOI
TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.

1,858 citations

Journal ArticleDOI
TL;DR: This communication describes a fast algorithm for computing the kurtogram over a grid that finely samples the ( f, Δ f ) plane and the efficiency of the algorithm is illustrated on several industrial cases concerned with the detection of incipient transient faults.

1,130 citations

Journal ArticleDOI
TL;DR: In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter based denoising methods are compared based on signals from mechanical defects, and the comparison result reveals that wavelet filters are more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet transform has a better performance on smooth signal detection.

1,104 citations

Journal ArticleDOI
TL;DR: In this article, the spectral kurtosis (SK) was used to detect and characterize nonstationary signals in the presence of strong masking noise and to detect incipient faults in rotating machines.

1,067 citations

Journal ArticleDOI
TL;DR: A formalisation of the spectral kurtosis by means of the Wold–Cramer decomposition of “conditionally non-stationary” processes is proposed, which engenders many useful properties enjoyed by the SK.

974 citations