Real-time fault diagnosis and trend prediction of rolling bearings based on resampling dynamic time warping and time-domain indicator analysis

A sudden fault in a rolling bearing (RB) results in a large amount of downtime, which increases the cost of operation and maintenance. In this paper, a real-time diagnosis and trend prediction method for RBs is proposed. In this method, a novel resampling dynamic time warping (RDTW) algorithm is presented and two new time-domain indicators (NTDIRs) called TALAP and TRCKT are defined, which can describe the wear degree and trend of an RB inner ring wear fault (IRWF). TALAP and TRCKT are proposed by comprehensively considering the stability and sensitivity of existing time-domain indicators (TDIRs). First, RDTW is used to align the healthy vibration signal with the fault vibration signal. Then, the residual signal that can be used to monitor the running condition is obtained. TALAP and TRCKT of the residual signal are calculated to judge the degree of wear. When the wear limit is reached, a fault alarm is sent out and the downtime needed for replacement can be accurately indicated. The experimental results show that the method can perform accurate diagnosis and trend prediction of inner ring wear faults of RBs.

Oil health index calculation and incipient fault diagnosis in power transformers using fuzzy logic

In this paper, a new fuzzy logic (FL) model is proposed for assessing the health status of power transformers. In addition, the detection of incipient faults is achieved where two or more faults exist simultaneously. The process is carried out by integrating a fuzzy logic model with the conventional International Electric Committee (IEC) ratio codes method. As transformer oil insulation deteriorates, excess percentages of dissolved gases such as hydrogen, methane, ethane, acetylene and ethylene are induced within the trasnformer. The status of oil health is generally assessed using these gas concentrations. Therefore, in the proposed model, 31 fuzzy rules are designed based on the severity levels of these gases in order to determine the health index (HI) of the oil. Similarly, any incipient faults along with their severity are also detected using the proposed fuzzy logic model with 22 expert rules. To validate the proposed fuzzy logic model, the data for dissolved gases in 50 working transformers operated by the Himachal Pradesh State Electricity Board (HPSEB), India, are collected. Over the years, calculations for the health index have been performed using conventional dissolved gas analysis (DGA) interpretation methods. The shortcomings of these methods, such as non-reliability and inaccuracy, are successfully overcome using the proposed model. The detection of incipient faults is normally performed using key gas, Rogers ratios, the Duval triangle, Dornenburg ratios, modified Rogers ratios and the IEC ratio codes methods. The shortcomings of these conventional ratio code methods in identifying incipient faults in some typical cases, ie multiple incipient fault cases, are overcome by the proposed fuzzy logic model.

Research on gearbox impact feature extraction method based on improved ESMD

A gearbox vibration signal contains non-linear impact characteristics and the significant feature information tends to be overwhelmed by other interference components, which make it difficult to extract the typical fault features fully and effectively. Aiming at the key issue of how to effectively extract the impact characteristics, a fault diagnosis method based on improved extreme symmetric mode decomposition (ESMD) and a support vector machine (SVM) is proposed in this paper. The vibration signal is adaptively decomposed into multiple intrinsic mode function (IMF) components by the improved ESMD and then a certain number of components are selected with the maximum kurtosis-envelope spectrum index. The singular spectral entropy, energy entropy and permutation entropy of each component are applied to construct the feature vector set, in which the dimensionality of the set is reduced with the distance separability criterion. Finally, the dimension-reduced feature vector set is input into the SVM for pattern recognition. Dynamic simulation and experimental gearbox research show that the improved ESMD method can extract and identify gearbox fault information effectively.

Study on stress testing of ferromagnetic materials based on magnetic anisotropy

The stress testing and evaluation of ferromagnetic materials that are widely applied in engineering has always been a focus of, and presented difficulties for, non-destructive testing. As there is still no effective method for detecting the stress of ferromagnetic materials, this paper puts forward the idea of applying the magnetic anisotropy method based on the inverse magnetostriction effect in stress testing of ferromagnetic materials. According to the principle of the magnetic anisotropy method, this paper discusses the development of Mn-Zn ferrite probes of three different structures, the construction of a magnetic anisotropy testing system comprising an excitation system, a signal collecting system and a signal processing system and the way in which a testing experiment was conducted on a 16MnR steel plate specimen under different conditions of stress, frequency and excitation voltage. All three types of probe can effectively determine the stress location of the specimen and present different phenomena and characteristics of the test. According to the experiment, significant correlation is seen between the stress and the magnetic signal, which provides a new idea for stress testing of ferromagnetic materials.

Research and application of infrared thermography testing technology for large-scale amusement rides

The operation of large-scale amusement rides is directly related to the safety of the passengers. When an accident occurs involving an amusement ride, the social impact is extremely negative. Therefore, the safety requirements for large-scale amusement rides are extremely high. Condition monitoring and fault diagnosis technologies provide effective ways to ensure the safe operation. Infrared thermal imaging is a common inspection and monitoring technology, which is widely used in electrical and hydraulic machinery systems. However, there is little literature about the application of infrared thermography (IRT) in large-scale amusement rides and a lack of analysis and evaluation methods for infrared inspection results. In order to expand this technology to the field of large-scale amusement rides, it is necessary to research the temperature increase characteristics of key components and develop corresponding technical standards. In the current study, the temperature variation characteristics of the electrical and hydraulic systems of large-scale amusement rides are first examined. Subsequently, the first IRT testing standard for amusement rides in China is described, including its main content and technical requirements. Two test cases are provided in this study in order to illustrate the practicability and reliability of infrared thermography testing technology in the large-scale amusement rides industry.

A novel detection of weld defects by magneto-optical imaging under combined magnetic field

To overcome the shortcomings of existing magneto-optical imaging, such as the saturation of an image under a constant magnetic field and the ambiguity of an image under an alternating magnetic field, imaging using a combined magnetic field is presented in this research. Weld defect samples include a laser-cut groove, a wire-cut penetrating groove, a pit and a Z-shaped crack. Magneto-optical imaging experiments were carried out under different magnetic fields. Contour extraction and standard deviation calculations were carried out for all magneto-optical images and the maximum standard deviation of the laser-cut groove under an alternating magnetic field was 20.9, which was less than the maximum value of 37.4 under a combined magnetic field. The experimental results show that the contrast of a magneto-optical image obtained under the combined magnetic field is greater than that obtained under the alternating magnetic field for all defects. The proposed combined magnetic field could optimise the magneto-optical imaging effect for weld defects under the existing excitation method to a certain extent.

Material characterisation by enhanced resolution in non-stationary thermal wave imaging

In the recent past, quadratic frequency-modulated thermal wave imaging (QFMTWI) has been advanced with a chirp z-transform (CZT)-based processing approach to facilitate enhanced subsurface anomaly detection, depth quantification and material property estimation with enhanced depth resolution. In the present study, the applicability of CZT-based phase analysis for foreign object defect detection in a structural steel sample using QFMTWI is validated through finite element-based numerical modelling rather than experimental verification due to limited available resources. Furthermore, the enhanced defect detection capability of the CZT phase approach is qualitatively compared with the frequency- and time-domain phase approaches using the defect signal-to-noise ratio (SNR) as a quality metric. Also, an empirical relationship between the observed phases and the thermal reflection coefficient is obtained, which recommends the CZT phase as a prominent approach for foreign material defect detection.

Defect type identification of thin-walled stainless steel seamless pipe based on eddy current testing

Aimed at eddy current detection of defects in thin-walled stainless steel seamless pipes, an effective detection method for identifying defect types is proposed. First, the empirical mode decomposition (EMD) method is used to process the collected eddy current signals and obtain the principal intrinsic mode function (IMF) components of different defects. The Hilbert-Huang transform (HHT) is used to extract the frequency-domain features of the principal IMF components, which are combined with the time-domain features to form an effective defect feature vector. Then, principal component analysis (PCA) is used to reduce the dimensions of the defect feature vector and the redundant information is removed to obtain the principal component vector of the defect. Finally, two radial basis function (RBF) neural networks are used to identify and classify the defect types and three error evaluation indicators are selected to evaluate the performance of the classification network models.

Experimental three-point bending test of glass fibre aluminium honeycomb sandwich panel with acoustic emission damage assessment

Due to their complexity, detecting and analysing damage modes in composite honeycomb sandwich panels can be difficult. This article describes the way in which a three-point bending test (3PBT) was performed on a glass fibre aluminium honeycomb sandwich panel (HSP). Acoustic emission (AE) was used to identify damage signals, which were then analysed to determine the positions and characteristics of defects. To locate damage positions, Delta-T mapping was used. The test load was progressively applied in three phases, with the specimen being inspected visually during each phase. A scanning electron microscope (SEM) showed that the most significant damage was local crushing under the test load, which caused matrix cracking, fibre breakage and pull-out. Damage progression and the damage mode were detected using the cumulative energy and frequency spectra of the AE sources for each phase. Matrix cracking frequencies ranged from 30 kHz to 100 kHz, while fibre damage modes ranged from 157 kHz to 322 kHz. The findings highlighted the utility of Delta-T mapping in locating damage positions on sandwich structures under testing. The investigation also emphasised the value of studying frequency spectra and cumulative energy when analysing AE signals.

Coded excitation for low-power guided ultrasonic wave inspection in safety-critical industries: case studies

High-power ultrasonic non-destructive evaluation (NDE) poses significant threats to intrinsic safety. It may lead to hazards in critical industrial applications, especially in oil & gas refineries, high-energy material technologies and the aerospace and aviation industries. Typically, industries employ various certifications and undertake several safety protocols to suppress the likelihood of industrial hazards. In order to satisfy safety standards for operating high-power equipment close to potential explosives and inflammable substances, industries direct large sums of investment into making these inspection systems intrinsically safe by designing complex structures and devising procedures to isolate such equipment from the system or process entirely. However, the uncertainty regarding the effectiveness of such protective measures results in a persisting difficulty in obtaining plant safety certifications and approvals. In this paper, the application of a coded excitation method to make inspection systems intrinsically safe and easily certifiable is explored. Using a pulse compression-based signal processing technique called coded excitation, it has been made possible to achieve non-contact transduction (electromagnetic acoustic transduction and air-coupled transduction) in transmitreceive mode with excitation as low as 0.5 Vpp (peak-to-peak supply voltage). This work reports on the application of coded excitation in bringing down the transduction power requirements for guided ultrasonic wave inspection, thereby making it possible to formulate new inspection applications at very low power, particularly in safety-critical industries.