The bearing vibration signal is a rich dynamic symptom of wear, and the rolling presents chaotic characteristics. Input output variables can be constructed through phase space reconstruction, imported into prediction model for prediction. accuracy extreme learning machine (ELM) model, Kriging RBF are compared, results show that ELM has higher accuracy, so chaos used to predict future time serie...

Magnetic bearings use magnetic forces to support various machine components. Because of the non-contact nature of this type of suspension, magnetic bearing technology offers a number of significant advantages over conventional bearings such as rolling element and fluid film bearings. An active magnetic bearing basically consists of an electromagnetic actuator, position sensors, power amplifiers...

Abstract:
 Rolling element bearings are widely used in many mechanical systems and affect their safe operation reliability. The use of an integral Oil Debris Sensor the lubrication system allows continuous monitoring chips particles originating from evolving failure oil wetted components.
 This paper examines mass loss Monitoring sensor as a Health indicator to assess damage severity ...

CONTACT FATIGUE is a surface-pitting-type failure commonly found in ball or roller bearings. This type of failure can also be found in gears, cams, valves, rails, and gear couplings. Contact fatigue has been identified in metal alloys (both ferrous and nonferrous) and in ceramics and cermets. Contact fatigue differs from classic structural fatigue (bending or torsional) in that it results from ...

Rolling-element bearings are one of the most important elements when predicting noise rotating machinery. As a major connecting point between and non-rotating parts, their dynamic properties have to be accurately known. In this investigation we present bottom-up approach characterising dynamics rolling-element bearing. A special test device was designed built assess quality well-established ana...

Purpose – To present a new application of Pursuit based analysis for diagnosing rolling element bearing faults. Methodology Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique-based fault detection and identification. This paper presents a compar...

This paper presents a method of fault diagnosis for a rolling bearing used in a reciprocating machine by the adaptive filtering technique and a fuzzy neural network. The adaptive filtering is used for noise cancelling and feature extraction from vibration signal measured for the diagnosis. A fuzzy neural network is used to automatically distinguish the fault types of a bearing by time domain fe...

This paper proposes a feature extraction method based on information theory for fault diagnosis of reciprocating machinery. A method to obtain symptom parameter waves is defined in the time domain using the vibration signals, and an information wave is presented based on information theory, using the symptom parameter waves. A new way to determine the difference spectrum of envelope information...

This paper proposes a weak signal detection strategy for rolling element bearing fault diagnosis by investigating a new mechanism to realize stochastic resonance (SR) based on the Woods–Saxon (WS) potential. The WS potential has the distinct structure with smooth potential bottom and steep potential wall, which guarantees a stable particle motion within the potential and avoids the unexpected n...

This paper presents a mathematical model to investigate the nonlinear dynamic behavior of a high speed rotor-bearing system due to defects of rolling elements. Two defects have been considered for the study, one defect as off-sized rolling elements and other defect as rolling element waviness. In the formulation, the contacts between rolling elements and inner/outer races are considered as nonl...