Identifiability Considerations for Rotating Machine Fault Diagnosis and Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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Development of a Master-Slave Architecture Handheld Rotating Machine Fault Diagnosis Instrument

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.141.244 ◽

2011 ◽

Vol 141 ◽

pp. 244-250

Author(s):

Jian Wan ◽

Tai Yong Wang ◽

Jing Chuan Dong ◽

Pan Zhang ◽

Yan Hao

Keyword(s):

Fault Diagnosis ◽

Signal Integrity ◽

Module Design ◽

Time Performance ◽

Rotating Machine ◽

Machine Fault Diagnosis ◽

Slave Processor ◽

Design Idea ◽

Machine Fault ◽

Communication Methods

To insure that sampling signal integrity, accuracy and real-time performance can adapt to the development of rotating machine fault diagnosis technology, a master-slave architecture handheld rotating machine fault diagnosis instrument was developed based on S3C2410 ARM IC and TMS320VC5509A DSP IC. It provided an effective method for the field monitoring and diagnosis of the large rotating machine. The whole design idea and the structure of the hardware and the software were systematically introduced. The paper focused on the master-slave architecture design of the hardware, the communication methods between the master and the slave processor, and the signal pretreatment module design. Put into practice, the practicability, reliability and stability of the instrument were confirmed.

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Rotating machine fault diagnosis using dimension reduction with linear local tangent space alignment

Measurement ◽

10.1016/j.measurement.2013.04.061 ◽

2013 ◽

Vol 46 (8) ◽

pp. 2525-2539 ◽

Cited By ~ 28

Author(s):

Feng Li ◽

Baoping Tang ◽

Rongsong Yang

Keyword(s):

Fault Diagnosis ◽

Dimension Reduction ◽

Tangent Space ◽

Rotating Machine ◽

Machine Fault Diagnosis ◽

Machine Fault ◽

Local Tangent

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Machine Fault Diagnosis and Prognosis using Self-Organizing Map

Advances in Data Mining and Database Management - Biologically-Inspired Techniques for Knowledge Discovery and Data Mining ◽

10.4018/978-1-4666-6078-6.ch006 ◽

2014 ◽

pp. 129-148

Author(s):

Kesheng Wang ◽

Zhenyou Zhang ◽

Yi Wang

Keyword(s):

Fault Diagnosis ◽

Condition Monitoring ◽

Manufacturing Systems ◽

Maintenance Scheduling ◽

Self Organizing Map ◽

Corrective Maintenance ◽

Diagnosis And Prognosis ◽

Machine Fault ◽

Fault Diagnosis And Prognosis ◽

Self Organizing

This chapter proposes a Self-Organizing Map (SOM) method for fault diagnosis and prognosis of manufacturing systems, machines, components, and processes. The aim of this work is to optimize the condition monitoring of the health of the system. With this method, manufacturing faults can be classified, and the degradations can be predicted very effectively and clearly. A good maintenance scheduling can then be created, and the number of corrective maintenance actions can be reduced. The results of the experiment show that the SOM method can be used to classify the fault and predict the degradation of machines, components, and processes effectively, clearly, and easily.

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Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance

Journal of Vibration and Acoustics ◽

10.1115/1.4030346 ◽

2015 ◽

Vol 137 (5) ◽

Cited By ~ 41

Author(s):

Siliang Lu ◽

Qingbo He ◽

Haibin Zhang ◽

Fanrang Kong

Keyword(s):

Fault Diagnosis ◽

Signal Detection ◽

Stochastic Resonance ◽

Delayed Feedback ◽

Weak Signal ◽

Noise Interference ◽

Rotating Machine ◽

Machine Fault Diagnosis ◽

Machine Fault ◽

Time Delayed Feedback

The fault-induced impulses with uneven amplitudes and durations are always accompanied with amplitude modulation and (or) frequency modulation, which leads to that the acquired vibration/acoustic signals for rotating machine fault diagnosis always present nonlinear and nonstationary properties. Such an effect affects precise fault detection, especially when the impulses are submerged in heavy background noise. To address this issue, a nonstationary weak signal detection strategy is proposed based on a time-delayed feedback stochastic resonance (TFSR) model. The TFSR is a long-memory system that can utilize historical information to enhance the signal periodicity in the feedback process, and such an effect is beneficial to periodic signal detection. By selecting the proper parameters including time delay, feedback intensity, and calculation step in the regime of TFSR, the weak signal, the noise, and the potential can be matched with each other to an extreme, and consequently a regular output waveform with low-noise interference can be obtained with the assistant of the distinct band-pass filtering effect. Simulation study and experimental verification are performed to evaluate the effectiveness and superiority of the proposed TFSR method in comparison with a traditional stochastic resonance (SR) method. The proposed method is suitable for detecting signals with strong nonlinear and nonstationary properties and (or) being subjected to heavy multiscale noise interference.

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