Basic research on machinery fault diagnostics: Past, present, and future trends

doi:10.1007/s11465-018-0472-3

Front. Mech. Eng.

2018, Vol. 13

Issue (2) : 264-291 https://doi.org/10.1007/s11465-018-0472-3

REVIEW ARTICLE

Basic research on machinery fault diagnostics: Past, present, and future trends

Xuefeng CHEN^1,²(

), Shibin WANG^1,², Baijie QIAO^1,², Qiang CHEN^1,²

¹. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
². School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

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Abstract

Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

Keywords fault diagnosis fault mechanism feature extraction signal processing intelligent diagnostics

Corresponding Author(s): Xuefeng CHEN

Just Accepted Date: 25 September 2017 Online First Date: 01 February 2018 Issue Date: 16 March 2018

Cite this article:

Xuefeng CHEN,Shibin WANG,Baijie QIAO, et al. Basic research on machinery fault diagnostics: Past, present, and future trends[J]. Front. Mech. Eng., 2018, 13(2): 264-291.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-018-0472-3
https://academic.hep.com.cn/fme/EN/Y2018/V13/I2/264

Fig.1 Scope and basic research directions of machinery fault diagnostics (based on Refs. [¹,²])

Fig.2 OH-58A main rotor helicopter transmission faults [³,⁴]. (a) Spalled planetary bearing race; (b) spalled sun gear; (c) scored spiral bevel face gear

Fig.3 Worn teeth on a faulty gear in the milling stand of a hot strip milling production line [⁵,⁶]. (a) Front view; (b) back view

Fig.4 Machine faults in transmission systems of wind turbines. (a) Scratches on bearing race; (b) spalls on gear teeth surface; (c) abrasion on gear teeth top

Sensor	Main application	Advantages	Disadvantages
Ultrasonic probe	Flaw detection; distance and thickness measurements	Exact and efficient	Limited by the shapes of surfaces and the density or consistency of the material
Acoustic emission (AE) sensor	Crack growth, friction, delamination and matrix cracking detection	Detect, locate and characterize damage	Changes in physical properties only
Magnetic sensor	Speed, motion and position measurements; crack or large deformation detection with magnetic leakage	Efficient	Magnetic field required; expensive
Eddy-current transducer	Displacement, distance and position, oscillation and vibration measurements	Useful for demanding industrial environments; high resolution and temperature stability; high-frequency response	Only for electrically conductive materials; expensive
Accelerometer	Shock, vibration and acceleration measurements	High-frequency response; simple and reliable	Expensive
Strain gauge	Deformation and strain measurements	Cheap	Low-frequency response
Shape memory alloy	Deformation detection; active control	Fast response to change in temperature	Low-frequency response; structural fatigue and functional fatigue
Laser interferometer	Derivation or displacement measurements	High precision	Very expensive
Fiber-optic sensor	Strain, displacement, pressure and temperature measurements	Small size; high precision; immune to electromagnetic interference	Expensive
Electromagnetic acoustic transducer	Flaw detection; thickness measurements	Useful for automated inspection, and hot, cold, clean, or dry environments	Limited to metallic or magnetic products; low transduction efficiency
Piezoelectric lead zirconate titanate (PZT) element	Active sensor; vibration and crack detection	High-frequency response; cheap	Cannot be used for truly static measurements; drop in internal resistance and sensitivity at elevated temperature
PZT paint/polyvinylidene fluoride (PVDF) piezoelectric films	Vibration and crack detection	High-frequency response; cheap	Drop in internal resistance and sensitivity at elevated temperature
Laser Doppler velocimetry (LDV)	Velocity measurement	Absolute, linear with velocity and requires no pre-calibration; non-contact measurement	Expensive
Digital image correlation (DIC)	Deformation, displacement, strain, and optical flow measurements	Ease of implementation and use; non-contact measurement	? Cannot measure existing damage

Tab.1 Comparison of different types of sensors used in machinery fault diagnosis

Fig.5 (a) The time domain signal and (b) the frequency spectrum of the collected vibration signal; (c) kurtogram distribution using the original concept of kurtosis; (d) kurtogram distribution using an improved “spatial-spectral ensemble kurtosis”; (e) the retrieved fault features and (f) its envelope spectrum

Fig.6 Vibration signals of the gearbox and the extracted three subcomponents through the sparse diagnosis technique. (a) The original vibration signals; (b) harmonic components; (c) impulsive components; (d) residual components

Fig.7 Hilbert envelope spectrum of the original vibration signals and the extracted three subcomponents via the sparse diagnosis technique. (a) Original vibration signals; (b) harmonic components; (c) impulsive components; (d) residual components

Fig.8 MSWT representation of vibration signal of the dual-rotor turbofan engine. The LPR and HPR represent the “low-pressure rotor” and “high-pressure rotor”, respectively. The reconstructed signal shows the evidence for vibration jumping fault in the engine, as indicated by the arrow T1 [¹⁶³]

Fig.9 Sponsored programs for machinery fault diagnosis since 2006 [²⁷⁴]

Tab.2 Sponsored Key Programs for machinery fault diagnosis within 5 years [²⁷⁴]

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