Optimal Sub-Band Analysis Based on the Envelope Power Spectrum for Effective Fault Detection in Bearing under Variable, Low Speeds.
Nguyen Hung Ngoc,Kim Jaeyoung,Kim Jong-Myon
Sensors (Basel, Switzerland)
Early identification of failures in rolling element bearings is an important research issue in mechanical systems. In this study, a reliable methodology for bearing fault detection is proposed, which is based on an optimal sub-band selection scheme using the discrete wavelet packet transform (DWPT) and envelope power analysis techniques. A DWPT-based decomposition is first performed to extract the characteristic defect features from the acquired acoustic emission (AE) signals. The envelope power spectrum (EPS) of each sub-band signal is then calculated to detect the characteristic defect frequencies to reveal abnormal symptoms in bearings. The selection of an appropriate sub-band is essential for effective fault diagnosis, as it can reveal intrinsically explicit information about different types of bearing faults. To address this issue, we propose a Gaussian distribution model-based health-related index (HI) that is a powerful quantitative parameter to accurately estimate the severity of bearing defects. The most optimal sub-band for fault detection is determined using two dimensional (2D) visualization analysis. The efficiency of the proposed approach is validated using several experiments in which different defect conditions are identified under variable, and low operational speeds.
10.3390/s18051389
Deep Learning-Based Bearing Fault Diagnosis Method for Embedded Systems.
Pham Minh Tuan,Kim Jong-Myon,Kim Cheol Hong
Sensors (Basel, Switzerland)
Bearing elements are vital in induction motors; therefore, early fault detection of rolling-element bearings is essential in machine health monitoring. With the advantage of fault feature representation techniques of time-frequency domain for nonstationary signals and the advent of convolutional neural networks (CNNs), bearing fault diagnosis has achieved high accuracy, even at variable rotational speeds. However, the required computation and memory resources of CNN-based fault diagnosis methods render it difficult to be compatible with embedded systems, which are essential in real industrial platforms because of their portability and low costs. This paper proposes a novel approach for establishing a CNN-based process for bearing fault diagnosis on embedded devices using acoustic emission signals, which reduces the computation costs significantly in classifying the bearing faults. A light state-of-the-art CNN model, MobileNet-v2, is established via pruning to optimize the required system resources. The input image size, which significantly affects the consumption of system resources, is decreased by our proposed signal representation method based on the constant-Q nonstationary Gabor transform and signal decomposition adopting ensemble empirical mode decomposition with a CNN-based method for selecting intrinsic mode functions. According to our experimental results, our proposed method can provide the accuracy for bearing faults classification by up to 99.58% with less computation overhead compared to previous deep learning-based fault diagnosis methods.
10.3390/s20236886
Bearing Fault Detection Based on Empirical Wavelet Transform and Correlated Kurtosis by Acoustic Emission.
Gao Zheyu,Lin Jing,Wang Xiufeng,Xu Xiaoqiang
Materials (Basel, Switzerland)
Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.
10.3390/ma10060571
Ultrasonic sensitivity of strain-insensitive fiber Bragg grating sensors and evaluation of ultrasound-induced strain.
Tsuda Hiroshi,Kumakura Kenji,Ogihara Shinji
Sensors (Basel, Switzerland)
In conventional ultrasound detection in structures, a fiber Bragg grating (FBG) is glued on or embedded in the structure. However, application of strain to the structure can influence the sensitivity of the FBG toward ultrasound and can prevent its effective detection. An FBG can work as a strain-insensitive ultrasound sensor when it is not directly glued to the monitored structure, but is instead applied to a small thin plate to form a mobile sensor. Another possible configuration is to affix an FBG-inscribed optical fiber without the grating section attached to the monitored structure. In the present study, sensitivity to ultrasound propagated through an aluminum plate was compared for a strain-insensitive FBG sensor and an FBG sensor installed in a conventional manner. Strains induced by ultrasound from a piezoelectric transducer and by quasi-acoustic emission of a pencil lead break were also quantitatively evaluated from the response amplitude of the FBG sensor. Experimental results showed that the reduction in the signal-to-noise ratio for ultrasound detection with strain-insensitive FBG sensors, relative to traditionally-installed FBG sensors, was only 6 dB, and the ultrasound-induced strain varied within a range of sub-micron strains.
10.3390/s101211248
Laser Self-Mixing Fiber Bragg Grating Sensor for Acoustic Emission Measurement.
Liu Bin,Ruan Yuxi,Yu Yanguang,Xi Jiangtao,Guo Qinghua,Tong Jun,Rajan Ginu
Sensors (Basel, Switzerland)
Fiber Bragg grating (FBG) is considered a good candidate for acoustic emission (AE) measurement. The sensing and measurement in traditional FBG-based AE systems are based on the variation in laser intensity induced by the Bragg wavelength shift. This paper presents a sensing system by combining self-mixing interference (SMI) in a laser diode and FBG for AE measurement, aiming to form a new compact and cost-effective sensing system. The measurement model of the overall system was derived. The performance of the presented system was investigated from both aspects of theory and experiment. The results show that the proposed system is able to measure AE events with high resolution and over a wide dynamic frequency range.
10.3390/s18061956
Application of Fiber Bragg Grating Acoustic Emission Sensors in Thin Polymer-Bonded Explosives.
Fu Tao,Wei Peng,Han Xiaole,Liu Qingbo
Sensors (Basel, Switzerland)
Fiber Bragg grating (FBG) acoustic emission (AE) sensors have been used in many applications. In this paper, based on an FBG AE sensor, the sensing principle of the interaction between the AE wave and the sensor is introduced. Then, the directionality of the FBG AE sensor on the surface of a thin polymer-bonded explosive (PBX) material is studied. Finally, the time coefficient location method is proposed to correct the AE time detected by the FBG AE sensor, thereby improving the accuracy of location experiments.
10.3390/s18113778
Fibre Bragg Grating Based Acoustic Emission Measurement System for Structural Health Monitoring Applications.
Jinachandran Sagar,Rajan Ginu
Materials (Basel, Switzerland)
Fiber Bragg grating (FBG)-based acoustic emission (AE) detection and monitoring is considered as a potential and emerging technology for structural health monitoring (SHM) applications. In this paper, an overview of the FBG-based AE monitoring system is presented, and various technologies and methods used for FBG AE interrogation systems are reviewed and discussed. Various commercial FBG AE sensing systems, SHM applications of FBG AE monitoring, and market potential and recent trends are also discussed.
10.3390/ma14040897
Sensitivity Analysis of Acoustic Emission Detection Using Fiber Bragg Gratings with Different Optical Fiber Diameters.
Violakis Georgios,Le-Quang Tri,Shevchik Sergey A,Wasmer Kilian
Sensors (Basel, Switzerland)
Acoustic Emission (AE) detection and, in particular, ultrasound detection are excellent tools for structural health monitoring or medical diagnosis. Despite the technological maturity of the well-received piezoelectric transducer, optical fiber AE detection sensors are attracting increasing attention due to their small size, and electromagnetic and chemical immunity as well as the broad frequency response of Fiber Bragg Grating (FBG) sensors in these fibers. Due to the merits of their small size, FBGs were inscribed in optical fibers with diameters of 50 and 80 μm in this work. The manufactured FBGs were used for the detection of reproducible acoustic waves using the edge filter detection method. The acquired acoustic signals were compared to the ones captured by a standard 125 μm-diameter optical fiber FBG. Result analysis was performed by utilizing fast Fourier and wavelet decompositions. Both analyses reveal a higher sensitivity and dynamic range for the 50 μm-diameter optical fiber, despite it being more prone to noise than the other two, due to non-standard splicing methods and mode field mismatch losses. Consequently, the use of smaller-diameter optical fibers for AE detection is favorable for both the sensor sensitivity as well as physical footprint.
10.3390/s20226511