Wavelet Packet Energy Analysis Research Articles

Vibration Signal Analysis of Roadheader Based on Referential Manifold Learning

Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis.

Damage identification of Chi River bridge based on vehicle excitation and WPEVCR

In order to identify damage of Chi River bridge’s superstructure, a damage identification indicator is implemented in the field test, which involves the wavelet packet energy analysis with the feature of dynamic response signals caused by vehicle excitation. On the basis of the field test, a series of numeric models with varied service conditions were developed. The wavelet packet analysis method was utilized to decompose the bridge’s acceleration signals at both healthy and damaged status, and the values of Wavelet Packet Energy Variance Change Rate (WPEVCR) were obtained. Then, according to the acceleration signal data measured from the field test, the damage assessment of the condition of Chi River bridge was performed by means of the obtained WPEVCR. The results demonstrate the capability of WPEVCR in localizing and quantifying the bridge damage status. Moreover, another damage indicator based on the Hilbert-Huang Transform (HHT) has been also employed to verify the assessment of WPEVCR, and both damage identification approaches indicate that the Chi River bridge is in a healthy service condition.

Quality evaluation of organic protective paints using terahertz pulse imaging technology based on wavelet packet energy method

It is particularly important to make quality evaluation for paint spraying process. This work presented an efficient method based on wavelet packet energy for terahertz non-destructive evaluation of organic protective paint. The wavelet packet energy analysis algorithm was summarized. Various coating structures were experimentally investigated using terahertz pulsed imaging system and the relative wavelet energy and the Shannon entropy of the detected signal were calculated as wavelet energy indexes. The proposed algorithm was performed on the evaluation of coating unevenness and the identification of hidden corrosion defect. The results demonstrated that the wavelet energy indexes are sensitive to coating structure change when compared to the terahertz peak-finding method, and it allowed more accurate estimate of micro corrosion defect. Therefore, the proposed approach based on wavelet packet energy could be recommended for quick quality evaluation of organic coating with terahertz non-destructive technology.

Interface damage monitoring of assembled GFRP-UHPC composite beams using piezoelectric smart aggregate

Glass fiber reinforced plastic reinforcement (GFRP) and ultra-high performance concrete (UHPC) were combined into a new composite beam, which was applied in ocean engineering to improve the durability of structures. To enhance the stiffness and durability of composite beam and lower the cost of structure, prefabricated construction technology was adopted to reserve holes for pouring UHPC. Through the quasi-static test of prefabricated GFRP-UHPC composite beams, the interface between concrete and GFRP was monitored using piezoelectric smart aggregate. The damage index was obtained in accordance with wavelet packet energy analysis theory to examine the interface damage of prefabricated composite beams. Experimental results show that active monitoring of assembled GFRP-UHPC composite beams with piezoelectric smart aggregate can effectively reflect the degree of interface peeling damage of composite beams. The monitoring results reveal that interface damage of specimens with reserved continuous holes is less than that of specimens with reserved discontinuous holes. Moreover, peeling damage will occur not only between GFRP and UHPC, but also at the interface between concrete and UHPC.

Real-time monitoring of humidity inside concrete structures utilizing embedded smart aggregates

The moisture level of hydraulic concrete structures strongly influences their strength and durability, so the real-time monitoring of the concrete humidity is crucial to ensure the regular operation of such structures. To this end, this study used an active monitoring method to observe the concrete humidity in real time by embedding pairs of piezoelectric smart aggregates (SAs) in concrete. The monitoring platform based on smart aggregates was constructed and measured five concrete specimens of two dimensions. The time-domain signals of the concrete specimens at different water content levels were obtained, and the wavelet packet energy analysis characterized the sensor signal energy quantitatively. On this basis, a humidity index was defined to describe the variation in the humidity of the concrete specimens. The experimental results demonstrate that the humidity index is practicable to monitor and evaluate the humidity inside concrete structures. Compared with traditional methods for monitoring the moisture content, the proposed technique does not need manual external excitation and can realize real-time, lossless, and long-time monitoring of concrete humidity.

Health Diagnosis of Roadheader Based on Reference Manifold Learning and Improved K-Means

The safe and stable operation of roadheader is of great significance to the efficient and rapid production of a coal mine. Health diagnosis based on vibration signals has been studied in bearings and motors. Complex geological conditions and bad working environment lead to the characteristics of nonlinear and time-varying vibration signals of a roadheader. In this paper, a health state analysis method based on reference manifold (RM) learning and improved K-means clustering analysis was proposed; the method was verified by using the real-time collected roadheader cutting reducer fault signal. Firstly, the comparison signal and analysis signal were extracted from the actual collected vibration data of the roadheader, and the referential analysis samples were constructed through time domain and wavelet packet energy analysis. Then, the characteristic structure of the low-dimensional space of the referential analysis samples is obtained by Locally Linear Embedding (LLE), which is a method of manifold learning. Through the improved K-means clustering analysis method, the low-dimensional structure parameters were analyzed and the clustering effect index was obtained, which was used as the health evaluation index (HEI). Finally, the normal distribution model of the health evaluation index is established, and the confidence interval of the health evaluation index is determined, so as to realize the health state analysis of the roadheader and realize the fault warning function. Through the analysis of data of three sensors, the results show that the roadheader failed on the 15th day, which is consistent with the actual working condition. Through practical analysis, the effectiveness of the method was verified and provided a kind of fault analysis idea and method for equipment working under complex working conditions and the theoretical basis for fault type analysis.

Series Arc Fault Detection Based on Random Forest and Deep Neural Network

Series arc is prone to cause fire accidents, but its occurrences induced by different load types and connections make the detection challengeable. This paper proposes a series arc fault detection and location algorithm for multi-load circuit topology, especially for branch arc faults and nonlinear power loads. Several typical loads of paralleling connected are considered to measure the current changes caused by the arcing phenomenon at different positions. Different aspects of arc features are extracted by time-domain, frequency-domain, and wavelet packet energy analysis. A feature selection method based on random forest (RF) is adopted to determine the specific feature sets according to the reduction of Gini impurity. The integrated top ten features with a high correlation to the arc were selected for different combinations of loads and are input into the deep neural network (DNN) for calculating and training. Eventually, a comprehensive arc detection model with the function of detection and location determination for different load types is obtained. It proves that the proposed RF-DNN-based arc detection algorithm can identify and protect the series arc faults in multi-load scenarios efficiently and accurately to meet the practical needs.

Early Age Concrete Strength Monitoring Using Power Spectral Density and Wavelet Packet Analysis

Concrete is a complex building material. Under normal curing conditions, concrete strength shows a nonlinear development process at an early age (1∼28 d). In the first few days after the completion of pouring, the strength of concrete increases slowly. Subsequently, the strength of concrete increases rapidly, reaching about 90% of its age strength. Finally, its strength gradually stabilizes. This paper introduces the experiment of 28-day concrete age strength monitoring based on embedded piezoelectric smart aggregate (SA). Two piezoelectric SAs were embedded in a concrete-filled GFRP (glass fiber reinforced polymer) tube column, one of which emitted a sinusoidal sweep signal and the other SA received the signal. With the hydration reaction of concrete, the stress wave would be significantly different when passing through concrete, and the received signal is changing constantly. Through power spectral density and wavelet packet energy analysis, the monitoring signal of concrete age within 28 days was analyzed. The experimental results show that the wavelet packet energy and power spectral density of the sensor monitoring signal show a nonlinear growth trend with time during the concrete target age. It can be divided into three stages, and the fifth day and the fourteenth day are the demarcation point of energy growth. And the trend of energy change corresponds well with the change of actual concrete strength and age. Comparing and analyzing the received signal energy of the sensor and the power spectral density function of the stress wave signal of the concrete specimen, the trend of the amplitude in the natural frequency domain is found to be the same in the three stages.

Feature Extraction for Bearing Fault Detection Using Wavelet Packet Energy and Fast Kurtogram Analysis

An integrated method for fault detection of bearing using wavelet packet energy (WPE) and fast kurtogram (FK) is proposed. The method consists of three stages. Firstly, several commonly used wavelet functions were compared to select the appropriate wavelet function for the application of WPE. Then the analyzed signal is decomposed using WPE and the energy of each decomposed signal is calculated and selected for signal reconstruction. Secondly, the reconstructed signal is analyzed by FK to select the best central frequency and bandwidth for the band-pass filter. Finally, the filtered signal is processed using the squared envelope frequency spectrum and compared with the theoretical fault characteristic frequency for fault feature extraction. The procedure and performance of the proposed approach are illustrated and estimated by the simulation analysis, proving that the proposed method can effectively extract the weak transients. Moreover, the analysis results of gearbox bearing and rolling bearing cases show that the proposed method can provide more accurate fault features compared with the individual FK method.

Field Monitoring and Analysis of the Vibration of Stay Cables under Typhoon Conditions.

Structural health monitoring systems provide many advantages for full-scale measurements in bridge monitoring. In this study, a strong landing typhoon event recorded at the Jintang Bridge (Zhejiang Province, China) in 2019 was selected to study the nonstationary wind and cable vibration characteristics. To study the characteristics of the recorded typhoon, the time-varying mean wind was extracted based on the adaptive method of the wavelet-matrix transform. The nonstationary characteristics of Typhoon Lekima, including the turbulence intensity, gust factor, and fluctuating wind power spectral density, were analyzed and compared with the stationary model characteristics of a typhoon, and the typical characteristics and parameters were obtained. In addition, the measured vibration response of the cables was analyzed. The vibration characteristics of the cables and the energy distribution of the wind speed wavelet packet were investigated. The vibrations at different positions were compared. A power spectrum analysis and a wavelet packet energy analysis of the cable were performed. The results of this study can be used as a basis for wind-resistant design and performance evaluation of bridges under similar operational conditions.

Wavelets base Automatic Extraction Technology for Vibration Signal Characteristics of Hybrid Electric Vehicle Engine

Traditional automatic characteristic extraction technology of engine vibration signals for hybrid electric vehicles (HEV) only focuses on the analysis of engine vibration signals in time domain and frequency domain. Single time domain analysis or single frequency domain analysis cannot accurately analyse the vibration signals, while both time domain analysis and frequency domain analysis have cross-analysis. As a result, the analysis results are repetitive and conflicting, which makes it difficult to extract the characteristics of engine vibration signals. The final extraction accuracy is not high and the extraction efficiency is low. For this reason, an automatic characteristic extraction technology of HEV engine vibration signal based on wavelet packet energy analysis is proposed. Firstly, the mechanical vibration of engine is converted into corresponding voltage and current signals by various sensors and then converted into digital signals by A/D (analog/digital) conditioner. The data of vibration signals are often mixed with various noises, which have a great impact on the final analysis of vibration signals. Data interception and pre-filtering are adopted. Wave, zero-mean, elimination of trend term and elimination of staggered points are used to pre-treat the vibration signals with mixed noise. Short-Time Fourier Transform (STFT) algorithm is introduced to analyse the pre-processed engine vibration signals and the fundamental properties of the non-stationary vibration signals in actual operation of the engine are obtained. The energy distribution of the analysed engine vibration signal is calculated by the wavelet packet energy analysis method. The calculated parameters of the energy distribution of the wavelet packet are taken as the characteristic parameters of the vibration signal. The vibration signal characteristics of the engine are automatically extracted. The experiment is carried out in the form of comparison with the traditional method. The experimental results show that the time-frequency joint analysis applied in the proposed technology can accurately analyse the essential characteristics of the engine vibration signal of HEV. The wavelet packet energy analysis method can ensure the extraction accuracy of the engine vibration signal characteristics.

Fault Data Detection of Traffic Detector Based on Wavelet Packet in the Residual Subspace Associated with PCA

To improve the accuracy and efficiency of fault data identification of traffic detectors is crucial in order to decrease the probability of unexpected failures of the intelligent transportation system (ITS). Since convolutional fault data recognition based on traffic flow three-parameter law has a poor capability for multiscale of fault data, PCA (principal component analysis) is adopted for traffic fault data identification. This paper proposes the fault data detection models based on the PCA model, MSPCA (multiscale principal component analysis) model and improved MSPCA model, respectively. In order to improve the recognition rate of traffic detectors’ fault data, the improved MSPCA model combines the wavelet packet energy analysis and PCA to achieve traffic detector data fault identification. On the basis of traditional MSPCA, wavelet packet multi-scale decomposition is used to get detailed information, and principal component analysis models are established on different scale matrices, and fault data are separated by wavelet packet energy difference. Through case analysis, the feasibility verification of traffic flow data identification method is carried out. The results show that the improved method proposed in this paper is effective for identifying traffic fault data.

Study on the propagation law of tunnel blasting vibration in stratum and blasting vibration reduction technology

In order to explore the propagation law of blasting vibration velocity in strata and reduce the damage caused by blasting vibration to buildings, a series of blasting vibration tests were carried out in this paper based on the Dizong tunnel project. The affected area of tunnel blasting was divided into four areas according to observed data, respectively, the dangerous zone, the warning zone, the blasting control zone, and the safe zone. The power spectral, wavelet-energy, wavelet packet energy analysis, and other signal processing were programmed with the help of MATLAB to analyze the propagation law of blasting vibration of super-large section shallow tunnel in the stratum. The results show that the maximum particle vibration velocity at the ground surface decreased with distance. The particle vibration velocity in the z-direction was larger than that in the horizontal direction. The energy of blasting vibration mainly came from the explosion in the first cut blasting. Based on the monitoring data and vibration signal analysis, a blasting vibration reduction scheme was put forward. The maximum particle vibration velocity caused by this scheme was controlled at 1.12 cm/s, and the main frequency of blasting vibration was much higher than the natural vibration frequency of the building. The blasting vibration reduction scheme could control the delayed release of blasting energy and realize the vibration missing peak of blasting vibration, which could provide a reference for similar engineering construction in the future.

Testing for detection of crack in rotor using vibration analysis: an experimental approach

Purpose Many incidents of rotor failures are reported due to the development and propagation of the crack. Condition monitoring is adopted for the identification of symptoms of the crack at very early stage in the rotating machinery. Identification requires a reliable and accurate vibration analysis technique for achieving the objective of the study. The purpose of this paper is to detect the crack in the rotating machinery by measuring vibration parameters at different measurement locations. Design/methodology/approach Two different types of cracks were simulated in these experiments. Experiments were conducted using healthy shaft, crack simulated shaft and glued shaft with and without added unbalance to observe the changes in vibration pattern, magnitude and phase. Deviation in vibration response allows the identification of crack and its location. Initial data were acquired in the form of time waveform. Run-up and coast-down measurements were taken to find the critical speed. The wavelet packet energy analysis technique was used to get better localization in time and frequency zone. Findings The presence of crack changes the dynamic behavior of the rotor. 1× and 2× harmonic components for steady-state test and critical speed for transient test are important parameters in condition monitoring to detect the crack. To separate the 1× and 2× harmonic component in the different wavelet packets, original signal is decomposed in nine levels. Wavelet packet energy analysis is carried out to find the intensity of the signal due to simulated crack. Originality/value Original signals obtained from the experiment test set up may contain noise component and dominant frequency components other than the crack. Wavelet packets contain the crack-related information that are identified and separated in this study. This technique develops the condition monitoring procedure more specific about the type of the fault and accurate due to the separation of specific fault features in different wavelet packets. From the experiment end results, it is found that there is significant rise in a 2× energy component due to crack in the shaft. The intensity of a 1× energy component depends upon the shaft crack and unbalance orientation angle.

Damage Detection of Common Timber Connections Using Piezoceramic Transducers and Active Sensing.

Timber structures have been widely used due to their low-cost and environmental-friendly properties. It is essential to monitor connection damage to ensure the stability and safety of entire timber structures since timber connection damage may induce catastrophic incidents if not detected in a timely manner. However, the current investigations on timber connections focus on mechanical properties and failure modes, and the damage detection of timber connection receives rare attention. Therefore, in this paper, we investigate the damage detection of four common timber connections (i.e., the screw connection, the bolt connection, the decussation connection, and the tooth plate connection) by using the active sensing method. The active sensing method was implemented by using a pair of lead zirconate titanate (PZT) transducers: one PZT patch is used as an actuator to generate stress waves, and the other works as a sensor to detect stress waves after propagating across the timber connection. Based on the wavelet packet energy analysis, the signal energy levels of received stress waves under different damage extent are quantified. Finally, by comparing the signal energy between the intact status and the damage status of the timber connection, we find that the energy attenuates with increasing severity of the connection damage. The experimental results demonstrate that the active sensing method can realize real-time monitoring of timber connection damage, which can guide further investigations.

A PZT-Based Electromechanical Impedance Method for Monitoring the Soil Freeze⁻Thaw Process.

It is important to conduct research on the soil freeze–thaw process because concurrent adverse effects always occur during this process and can cause serious damage to engineering structures. In this paper, the variation of the impedance signature and the stress wave signal at different temperatures was monitored by using Lead Zirconate Titanate (PZT) transducers through the electromechanical impedance (EMI) method and the active sensing method. Three piezoceramic-based smart aggregates were used in this research. Among them, two smart aggregates were used for the active sensing method, through which one works as an actuator to emit the stress wave signal and the other one works as a sensor to receive the signal. In addition, another smart aggregate was employed for the EMI testing, in which it serves as both an actuator and a receiver to monitor the impedance signature. The trend of the impedance signature with variation of the temperature during the soil freeze–thaw process was obtained. Moreover, the relationship between the energy index of the stress wave signal and the soil temperature was established based on wavelet packet energy analysis. The results demonstrate that the piezoceramic-based electromechanical impedance method is reliable for monitoring the soil freezing and thawing process.

Damage Quantification with Embedded Piezoelectric Aggregates Based on Wavelet Packet Energy Analysis.

Cement-based components have been widely used in civil engineering structures. However, due to wearing and deterioration, the cement-based components may have brittle failure. To provide early warning and to support predictive reinforcement, the piezoelectric materials are embedded into the cement-based components to excite and receive elastic waves. By recognizing the abnormalities in the elastic waves, hidden damage can be identified in advance. However, few research has been published regarding the damage quantification. In this paper, the wavelet packet analysis is adopted to calculate the energy of the transmitted elastic waves based on the improved piezoelectric aggregates (IPAs). Due to the growth of the damage, less elastic waves can pass through the damage zone, decreasing the energy of the acquired signals. A set of cement beams with different crack depths at the mid-span is tested in both numerical and experimental ways. A damage quantification index, namely the wavelet packet-based energy index (WPEI), is developed. Both the numerical and experimental results demonstrate that the WPEI decreases with respect to the crack depth. Based on the regression analysis, a strong linear relationship has been observed between the WPEI and the crack depth. By referring to the linear relationship, the crack depth can be estimated by the WPEI with a good accuracy. The results demonstrated that the use of the IPAs and the WPEI can fulfill the real-time quantification of the crack depth in the cement beams.

Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis.

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

Evaluation of the Effect of Fly Ash on Hydration Characterization in Self-Compacting Concrete (SCC) at Very Early Ages Using Piezoceramic Transducers.

Nowadays, the industrial waste, Fly Ash (FA), as a mineral admixture or a replacement of cement for the production of self-compacting concrete (SCC) has been increasingly used, because of its benefits in enhancing both fresh and long-term concrete properties and in promoting environmental-friendly construction. In this study, the conventional cement was replaced by FA at different rates (0%, 20%, 40%, 60% of the cement mass) for the SCC mixtures. The early-age (0–24 h) SCC hydration, which is a complicated chemical reaction in pozzolanic behavior, was characterized by using a pair of piezoceramic Smart Aggregates (SAs). One SA works as an actuator and the other works as a sensor. A sweep sine signal from 100 Hz to100 kHz was used as the excitation signal, which is helpful to understand the quantitative influence of fly ash on the kinetics of SCC hydration. During the hydration reaction, the received electrical signal was continuously detected by the sensor. The experimental results showed that increasing the volume of fly ash resulted in longer pozzolanic reaction time in SCCs, which successfully reveals the effect of fly ash volume on the hydration behavior in early age (0–24 h) hydration. In order to quantitatively evaluate the hydration in the 0–24 h, based on the wavelet packet energy analysis, the hydration completion index (HCI) and normalized hydration completion index (NHCI) were defined. The experimental results showed that the NHCI can clearly reveal the hydration completion progress during the early hydration age (0–24 h). To validate the accuracy of the test results based on SAs, a series of mechanical tests for penetration resistance of SCCs with different volumes of fly ash were carried out. The results predicted by the signal based on SAs gave reasonable agreement with the test results of penetration resistance. It can be concluded that a successful investigation of the influence of fly ash on early-age SCC hydration response can be achieved based on the analysis of the received electrical signal using the proposed method and the important hydration characteristics, such as initial and final setting time, and can be approximately predicted by NHCI values.

Wavelet packet energy analysis of laser ultrasonic

In order to solve the problem of the rapid detection of aircraft engine in situ cracks, and get the relationship between feature information and detect depth, the laboratory experimental platform is built, laser is used to excite laser ultrasonic signals on a range of aviation aluminum plates with different depth defects, the collected signal is proposed by wavelet de-noising, and the band energy distribution of the reflected echo signal is studied by using wavelet packet. The results show that the energy of reflected echo signal is mainly concentrated in the S80～S87 band. When the depth of defect is 0.2 mm to 0.4 mm, the energy is mainly concentrated in the adjacent bands. When the depth of defect is 0.5 mm to 0.7 mm, the energy is mainly concentrated in the two bands. This method provides a way to quantify surface micro-defects by ultrasonic signals, which will lay a foundation for the future analysis of crack depth from band energy.