Deep Learning-Based Adaptive Neural-Fuzzy Structure Scheme for Bearing Fault Pattern Recognition and Crack Size Identification.

Bearings are complex components with onlinear behavior that are used to mitigate the effects of inertia. These components are used in various systems, including motors. Data analysis and condition monitoring of the systems are important methods for bearing fault diagnosis.

Prognosis of remaining bearing life with vibration signals using a sequential Monte Carlo framework.

This letter proposes a nonlinear hybrid model method to assess a bearing component's health for long-term prediction of the remaining useful life (RUL) before a breakdown occurs. This model uses neural training of a recursive extreme learning machine (RELM) core integrated with a Monte Carlo-based framework. Estimation of the model's parameters, along with the system states, is used to construct an updated model that is utilized for prediction.

Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features.

This paper proposes a reliable fault diagnosis model for a spherical storage tank. The proposed method first used a blind source separation (BSS) technique to de-noise the input signals so that the signals acquired from a spherical tank under two types of conditions (i.e.

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.

Non-Mutually Exclusive Deep Neural Network Classifier for Combined Modes of Bearing Fault Diagnosis.

The simultaneous occurrence of various types of defects in bearings makes their diagnosis more challenging owing to the resultant complexity of the constituent parts of the acoustic emission (AE) signals. To address this issue, a new approach is proposed in this paper for the detection of multiple combined faults in bearings. The proposed methodology uses a deep neural network (DNN) architecture to effectively diagnose the combined defects.