Machinery health prognostics: A systematic review from data acquisition to RUL prediction

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

Considering various health conditions under varying operational conditions, the mining sensitive feature from the measured signals is still a great challenge for intelligent fault diagnosis of spindle bearings. This paper proposed a novel energy-fluctuated multiscale feature mining approach based on wavelet packet energy (WPE) image and deep convolutional network (ConvNet) for spindle bearing fault diagnosis. Different from the vector characteristics applied in intelligent diagnosis of spindle bearings, wavelet packet transform is first combined with phase space reconstruction to rebuild a 2-D WPE image of the frequency subspaces. This special image can reconstruct the local relationship of the WP nodes and hold the energy fluctuation of the measured signal. Then, the identifiable characteristics can be further learned by a special architecture of the deep ConvNet. Other than the traditional neural network architecture, to maintain the global and local information simultaneously, deep ConvNet combines the skipping layer with the last convolutional layer as the input of the multiscale layer. The comparisons of clustering distribution and classification accuracy with six other features show that the proposed feature mining approach is quite suitable for spindle bearing fault diagnosis with multiclass classification regardless of the load fluctuation.

Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis

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Digital Signal Processing A Computer Based Approach

A review of stochastic resonance in rotating machine fault detection

Order tracking (OT), which is realized by signal sampling in equal-angle increment according to the measured rotating speed, is a powerful technique for rotating machine fault diagnosis under variable-speed condition. However, if the tachometer cannot be installed on the rotating machine or the speed signal is not available for some reasons, OT is difficult to be realized. This review summarizes recent advances in the development of tacholess speed estimation methods for OT with its applications to fault diagnosis. First, the basis of rotating speed estimation and OT is revisited. Then, the methods are categorized into three groups including vibration or sound signal, electrical motor current signal, and video stream according to the signal source from which the speed is estimated. The principle, implementation procedures, key techniques, along with the merits, and shortcuts of these methods are summarized and discussed in detail. Afterward, a contrastive case study using three kinds of methods is provided to intuitively illustrate the performances of OT along with the applications in motor bearing fault diagnosis. A bibliography of the recent publications related to this topic is also provided to facilitate the selection and improvement of the tacholess OT methods in fault diagnosis applications. Finally, the research prospects are discussed.

Siliang Lu

Papers

A review of stochastic resonance in rotating machine fault detection

Tacholess Speed Estimation in Order Tracking: A Review With Application to Rotating Machine Fault Diagnosis

Effects of underdamped step-varying second-order stochastic resonance for weak signal detection

Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis

Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning