Diagnosis of bearing defects under variable speed conditions using energy distribution maps of acoustic emission spectra and convolutional neural networks.

This letter proposes an efficient scheme for the early diagnosis of bearing defects using a convolutional neural network (CNN) and energy distribution maps (EDMs) of acoustic emission spectra. The CNN automates the process of feature extraction from the EDM. The features learned by the CNN are used by an ensemble classifier, that is, a combination of a multilayer perceptron that is integral to typical CNN architectures and a support vector machine to diagnose bearing defects.

A Reliable Health Indicator for Fault Prognosis of Bearings.

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature.

Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm.

This paper presents a novel method for diagnosing incipient bearing defects under variable operating speeds using convolutional neural networks (CNNs) trained via the stochastic diagonal Levenberg-Marquardt (S-DLM) algorithm. The CNNs utilize the spectral energy maps (SEMs) of the acoustic emission (AE) signals as inputs and automatically learn the optimal features, which yield the best discriminative models for diagnosing incipient bearing defects under variable operating speeds. The SEMs are two-dimensional maps that show the distribution of energy across different bands of the AE spectrum.

Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine.

Incipient defects in bearings are traditionally diagnosed either by developing discriminative models for features that are extracted from raw acoustic emission (AE) signals, or by detecting peaks at characteristic defect frequencies in the envelope power spectrum of the AE signals. Under variable speed conditions, however, such methods do not yield the best results. This letter proposes a technique for diagnosing incipient bearing defects under variable speed conditions, by extracting features from different sub-bands of the inherently non-stationary AE signal, and then classifying bearing defects using a weighted committee machine, which is an ensemble of support vector machines and artificial neural networks.

Estimating the remaining useful life of bearings using a neuro-local linear estimator-based method.

Estimating the remaining useful life (RUL) of a bearing is required for maintenance scheduling. While the degradation behavior of a bearing changes during its lifetime, it is usually assumed to follow a single model. In this letter, bearing degradation is modeled by a monotonically increasing function that is globally non-linear and locally linearized.

Rotational speed invariant fault diagnosis in bearings using vibration signal imaging and local binary patterns.

Structural vibrations of bearing housings are used for diagnosing fault conditions in bearings, primarily by searching for characteristic fault frequencies in the envelope power spectrum of the vibration signal. The fault frequencies depend on the non-stationary angular speed of the rotating shaft. This paper explores an imaging-based approach to achieve rotational speed independence.