Frequency Band Energy Research Articles

Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve

A high-speed solenoid valve is a key component of the braking system. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging processes have remained major challenges. To address this problem, a method combining physical indices and data features is proposed to predict the failure type of solenoid valve. Firstly, the mechanism model of the solenoid valve is established and five physical indices are extracted from the driven current curve. Then, the frequency band energy characteristics are obtained from the current change rate curve of the solenoid valve by wavelet packet decomposition. Combining physical indices and frequency band energy characteristics into a comprehensive feature vector, we applied random forest to both predict and classify the failure type. We generate a data set consisting of 60 high-speed solenoid valves periodically switched under accelerated aging test conditions, including driven current, final failure type, and switching cycles. The prediction result shows that the proposed method achieves 95.95% and 94.68% precision for the two failures using the driven current data of the 3000th cycle and has better prediction performance than other algorithms.

Snapping shrimp noise detection and mitigation for underwater acoustic orthogonal frequency division multiple communication using multilayer frequency

This paper proposes Snapping Shrimp Noise (SSN) detection and corrupted Orthogonal Frequency Division Multiplexing (OFDM) reconstruction methods to increase Bit Error Rate (BER) performance when OFDM transmitted signal is corrupted by impulsive SSNs in underwater acoustic communications. The proposed detection method utilizes multilayer wavelet packet decomposition for detecting impulsive and irregularly concentrated and SSN energy in specific frequency bands of SSN, and the proposed reconstruction scheme uses iterative decision directed-subcarrier reconstruction to recover corrupted OFDM signals using multiple carrier characteristics. Computer simulations were executed to show receiver operating characteristics curve for the detection performance and BER for the reconstruction. The practical ocean experiment of SAVEX 15 demonstrated that the proposed method exhibits a better detection performance compared with conventional detection method and improves BER by 250% and 1230% for uncoded and coded data, respectively, compared with the conventional reconstruction scheme.

A Novel Method for Chaos Detection in Heavy Noisy Environments Based on Distribution of Energy

Detecting chaos in heavy-noise environments is an important issue in many fields of science and engineering. In this paper, first, a new criterion is proposed to recognize chaos from noise based on the distribution of energy. Then, a new method based on stationary wavelet transform (SWT) is presented for chaos detection that is recommended for data that contain more than 60% noise. This method is dependent on the distribution of signal’s energy in different frequency bands based on SWT for chaos detection which is robust to noisy environments. In this method, the effect of white noise and colored noise on the chaotic system is considered. As a case study, the proposed method is applied to detect chaos in two different oscillators based on memristor and memcapacitor. The simulation results are used to display the main points of the paper.

Mechanistic Characteristics of Double Dominant Frequencies of Acoustic Emission Signals in the Entire Fracture Process of Fine Sandstone

To determine the intrinsic relationship between the acoustic emission (AE) phenomenon and the fracture pattern pertaining to the entire fracture process of rock, the present paper proposed a multi-dimensional spectral analysis of the AE signal released during the entire process. Some uniaxial compression AE tests were carried out on the fine sandstone specimens, and the axial compression stress–strain curves and AE signal released during the entire fracture process were obtained. In order to deal with tens of thousands of AE data efficiently, a subroutine was programmed in MATLAB. All AE waveforms of the tests were denoised by wavelet threshold firstly. The fast Fourier transform (FFT) and wavelet packet transform (WPT) were applied to the denoised waveforms to obtain the dominant frequency, amplitude, fractal, and frequency band energy ratio distribution. The results showed that the AE signal in the entire fracture process of fine sandstone had a double dominant frequency band of the low and high-frequency bands, which can be subdivided into low-frequency low-amplitude, high-frequency low-amplitude, high-frequency high-amplitude, and low-frequency high-amplitude signals, according to the magnitude. The low-frequency amplitude relevant fractal dimension and the high-frequency amplitude relevant fractal dimension each had turning points that corresponded to significant decreases in the middle and end stages of loading, respectively. The frequency band energy was mainly concentrated in the range of 0–187.5 kHz, and the energy ratios of some bands had different turning points, which appeared before the complete failure of the rock. It is suggested that the multi-dimensional spectral analysis may understand the failure mechanism of rock better.

Sequential neural information processing in nidopallium caudolaterale of pigeons during the acquisition process of operant conditioning.

The avian nidopallium caudolaterale, a key region of information integration and processing, is considered to be playing an important role in operant conditioning acquisition and extinction. To reveal sequential neural information processing in the process, neural signals of different experimental periods (induction, acquisition, and extinction) from the nidopallium caudolaterale of pigeons were acquired and the energy of the specific frequency band was analyzed from the light stimulation input to the pecking action output. We found that during the induction period, the pigeons establish a relationship between the visual cue and decision behavior. The neural coding activities of pecking intention are earlier than that of light stimulation. Moreover, the neural coding activities of pecking intention move forward through strengthening and consolidation of the acquisition period. During the extinction period, the relationship of the visual cue and decision behavior is broken. The coding of light stimulation and pecking intention disappears gradually, and the disappearance of intention coding activities is earlier. The results show that there may be present an elaborate time-course contingency between the light stimulation and the pecking intention in the nidopallium caudolaterale. This study provides the electrophysiological experimental evidence for the dynamic coding mechanism of nidopallium caudolaterale.

Wave Measurements From Radar Tide Gauges

Currently the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is transitioning the primary water level sensor at most NWLON stations from an acoustic ranging system to microwave radars. With no stilling well and higher resolution of the open sea surface, microwave radars have the potential to provide real-time wave measurements at NWLON sites. Radar sensors at tide stations may offer a low cost, convenient way to increase nearshore wave observational coverage throughout the U.S. to support navigational safety and ocean research applications. Here we present the results of a field study comparing wave height measurements from four radar water level sensors with two different signal types (pulse and continuous wave swept frequency modulation-CWFM). A nearby bottom acoustic wave and current sensor is used as a reference. An overview of field setup and sensors will be presented, along with an analysis of performance capabilities of each radar sensor. The study includes results from two successive field tests. In the first, we examine the performance from a pulse microwave radar (WaterLOG H-3611) and two CWFM (Miros SM-94 and Miros SM-140). While both types of radars tracked significant wave height well over the test period, the pulse radar had less success resolving high frequency wind wave energy and showed a high level of noise towards the low frequency end of the spectrum. The pulse WaterLOG radar limitations were most apparent during times of high winds and locally developing seas. The CWFM radars demonstrated greater capability to resolve those higher frequency energies while avoiding low frequency noise. The initial field test results motivated a second field test, focused on the comparison of wave height measurements from two pulse radar water level sensors, the WaterLOG H3611 and the Endress and Hauser Micropilot FMR240. Significant wave height measurements from both radar water level sensors compared well to reference AWAC measurements over the test period, but once again the WaterLOG radar did not adequately resolve wind wave energy in high frequency bands and showed noise towards the low frequency end of the spectrum. The E+H radar demonstrated greater capability to measure waves.

Wave Measurements from Radar Tide Gauges.

Currently the NOAA Center for Operational Oceanographic Products and Services (CO-OPS) is transitioning the primary water level sensor at most NWLON stations from an acoustic ranging system to microwave radars. With no stilling well and higher resolution of the open sea surface, microwave radars have the potential to provide real-time wave measurements at NWLON sites. Radar sensors at tide stations may offer a low cost, convenient way to increase nearshore wave observational coverage throughout the U.S. to support navigational safety and ocean research applications. Here we present the results of a field study comparing wave height measurements from four radar water level sensors with two different signal types (pulse and continuous wave swept frequency modulation-CWFM). A nearby bottom acoustic wave and current sensor is used as a reference. An overview of field setup and sensors will be presented, along with an analysis of performance capabilities of each radar sensor. The study includes results from two successive field tests. In the first, we examine the performance from a pulse microwave radar (WaterLOG H-3611) and two CWFM (Miros SM-94 and Miros SM-140). While both types of radars tracked significant wave height well over the test period, the pulse radar had less success resolving high frequency wind wave energy and showed a high level of noise towards the low frequency end of the spectrum. The pulse WaterLOG radar limitations were most apparent during times of high winds and locally developing seas. The CWFM radars demonstrated greater capability to resolve those higher frequency energies while avoiding low frequency noise. The initial field test results motivated a second field test, focused on the comparison of wave height measurements from two pulse radar water level sensors, the WaterLOG H3611 and the Endress and Hauser Micropilot FMR240. Significant wave height measurements from both radar water level sensors compared well to reference AWAC measurements over the test period, but once again the WaterLOG radar did not adequately resolve wind wave energy in high frequency bands and showed noise towards the low frequency end of the spectrum. The E+H radar demonstrated greater capability to measure waves.

Complex response of an oscillating vertical cantilever with clearance

We study the dynamics of an elastic inverted pendulum with amplitude limiters excited horizontally. This particular model corresponds to a class of systems where a clearance is present naturally as an effect of imperfect clamping or it is included to tailor the response. We explore the complex responses of the system for a fixed value of amplitude clearance. The simulation and experimental results are analysed by a 0–1 test, Fourier, and wavelet transforms. The results show that the system can vibrate with subharmonic solution where the main response frequency of a flexible beam is 3 times lower than the excitaion frequency. We claim that an inverted pendulum with imperfect clamping of mechanical resonator can be used in broad frequency band energy harvesting.

Characterization of fibromyalgia using sleep EEG signals with nonlinear dynamical features

Fibromyalgia is an intense musculoskeletal pain causing sleep, fatigue, and mood problems. Sleep studies have suggested that 70%–80% of fibromyalgia patients complain of non-restorative sleep. The abnormalities in sleep have been implicated as both a cause and effect of the disease. In this paper, the electroencephalogram (EEG) signals of sleep stages 2 and 3 are used to classify the normal and fibromyalgia classes automatically. We have used various nonlinear parameters, namely sample entropy (SampEn), fractal dimension (FD), higher order spectra (HOS), largest Lyapunov exponent (LLE), Kolmogorov complexity (KC), Hurst exponent (HE), energy, and power in various frequency bands from the EEG signals. Then these features are subjected to Student's t-test to select the clinically significant features, and are classified using the support vector machine (SVM) classifier. Our proposed method can classify normal and fibromyalgia subjects using the stage 2 sleep EEG signals with an accuracy of 96.15%, sensitivity and specificity of 96.88% and 95.65%, respectively. Performance of the developed system can be improved further by adding more subjects in each class, and can be employed for clinical use.

Guided Wave-Based Monitoring of Evolution of Fatigue Damage in Glass Fiber/Epoxy Composites

This paper presents an experimental study on detecting and monitoring of evolution of fatigue damage in composites under cyclic loads by using guided waves. Composite specimens fabricated by glass fiber/epoxy laminates and surface mounted with piezoelectric wafers are fatigued under tension–tension loads. A laser extensometer is used to obtain the degradation of longitudinal stiffness of the specimens under fatigue states to reflect the accumulation of internal fatigue damage. Meanwhile, at different fatigue cycles, one wafer acts as actuator to excite diagnostic guided waves, and the other acts as sensor to receive corresponding response waves. These guided wave signals are then processed by wavelet packet transform to extract characteristic features of energies in multiple frequency bands. A statistical multivariate outlier analysis is then performed to determine the existence of fatigue damage and to characterize their evolution using Mahalanobis squared distance. Experimental results have demonstrated the potential applicability and effectiveness of guided waves for continuous monitoring of fatigue damage in composite structures.

A wavelet transform method for studying the energy distribution characteristics of microseismicities associated rock failure

Microseismicity signals released during rock failure process are firstly recorded using microseismicity monitoring system. A wavelet transform scheme is then developed on the basis of the discrete wavelet transform and implemented into MATLAB to study the energy distribution characteristics of the monitored microseismicity signals. The wavelet transform scheme decomposes the recorded microseismicity signals into various wavelets at seven scales and eight frequency bands. The microseismicity energy at each frequency band is then calculated by integrating the wavelets in each scale. It is found that, for the microseismicity signals recorded during the uniaxial loading of the granite, the microseismicity energies are mainly distributed between the bands 7.8125–15.625 kHz, 15.625–31.250 kHz and 31.25–62.5 kHz and the percentages of the released energies at these frequency bands are 8.24%, 62.72%, 28.08% of the total energies, respectively. The results reveal that the microseismicity energies at these levels are directly related to the damage mechanisms of the granite although further studies are need to identify the failure modes. Then these monitored signals were processed using wavelet transformation to find out the frequency distribution rule and the frequency band energy varying rule of the acoustic emission (AE) signals during the different rock damage and failure stages. The rock failure mechanism was interpreted from the perspective of the relationship between AE signal frequency change and crack propagation. The frequency band energy distribution histograms of the microseismicity signals at different damage stages were computed and drawn by the energy calculation method of wavelet transformation implemented into MATLAB. The energy percentage of the low frequency band (d7-a7) and that of the dominated frequency band (d4-d6) and their variation rule were analyzed especially. Accordingly, the critical damage point is that the low frequency energy percentage is above a certain threshold. This index could be used as the failure precursor criterion for rock mass instability monitoring and early warning, since it provided a theoretical guidance for evaluating internal damage of rock. Finally, it is concluded that the proposed wavelet transform method may provide a new mean for the characteristics analysis of the microseismicity signals recorded by the microseismicity monitoring system and may stimulate the application of the microseismicity monitoring technology in the geotechnical and mining engineerings through analyzing the energy of the microseismicity signals to understand the law of microseismicity emitted by rockmass.

Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results.

Multi-mapping fault diagnosis of high voltage circuit breaker based on mathematical morphology and wavelet entropy

Mechanical faults of high voltage circuit breakers (CBs) seriously affect the reliability of their operation, which may cause severe damage to power systems. In order to monitor operational conditions and detect mechanical faults of CBs, a multi-parameter monitoring system is designed and a fault diagnosis method based on multi-mapping is proposed. The paper focuses on the trip/close circuits, the spring-charging mechanism and the transmission mechanism, and obtains four current signals and a vibration signal that can reflect CB conditions. For the current signals, a morphological filter is used to remove noise and then characteristics of the waveforms' shape information are extracted. For vibration signals, the wavelet packet transform is used to decompose the signal into various frequency bands, and the sample entropy of the low frequency bands and the wavelet energy of the high frequency bands are calculated, respectively. Based on these feature parameters, a multi-mapping strategy is proposed for CB fault diagnosis. Laboratory experiments have been conducted to obtain on-site signals under various conditions, and experiment results have verified that monitoring the aforementioned signals and using the corresponding feature extraction and fault diagnosis methods, the mechanical faults of high voltage CBs can be effectively diagnosed.

Health Degradation Monitoring of Rolling Element Bearing by Growing Self- Organizing Mapping and Clustered Support Vector Machine

Health degradation monitoring of rolling element bearing (REB) is of great significance to ensure safety and availability of mechanical equipment. This paper presents a new online health degradation monitoring method of REB based on growing self-organizing mapping (GSOM) and clustered support vector machine (CSVM). In the proposed method, multidimensional health degradation features of the REB are extracted to reflect health degradation process, including time-domain statistical features, frequency spectrum features, intrinsic mode function energy features, wavelet packet frequency band energy features. Multiple GSOMs are utilized to adaptively fuse each kind of the extracted features for the health indices. CSVM is constructed to achieve accurate health status identification of the REB. A health degradation experimental case of the REB is analyzed to demonstrate the effectiveness of the proposed method. The results show that the proposed method has obvious superior performance compared to other existing methods.

Bearing Fault Identification Method Based on Collaborative Filtering Recommendation Technology

As the amount of data generated by monitoring the condition of rolling bearings is increasing, it has become a research hotspot in recent years to dig valuable information from massive data and identify unknown bearing states. In Internet technology, the collaborative filtering recommendation technology provides users with an intelligent means of filtering information. Aiming at the difficulty in designing the recommendation system scoring matrix in the field of fault diagnosis, we first obtain the bearing feature matrix based on the wavelet frequency band energy and then design a scoring matrix that accurately describes the bearing state; finally, we design a joint scoring matrix for bearing state identification by combining the matrix of these two different characteristics. After that, a collaborative filtering recommendation system for bearing state identification is proposed based on matrix factorization‐based collaborative filtering and gradient descent algorithm. This method is used to identify and verify two types of fault data of rolling bearing: different position faults and different types of faults on the outer ring. The results show that the accuracy of the two identifications has reached more than 90%.

On the Relationship Between the Electromagnetic Burst and Inductive Sensor Measurement of a Pulsed Plasma Accelerator

A remote and non-invasive diagnostic of the plasma focus using antennas is presented in this work. The main motivation is the application of such diagnostic in a miniaturized plasma accelerator, based on the plasma focus architecture, as a cube satellite thruster. The evaluation of this proposal was carried out measuring a hundred of joules plasma focus operation simultaneously with the inductive measurement and antennas. Three different antennas tuned in the ultra high frequency range were tested: a monopole, Vivaldi and helical. The high frequency transients detected with the antennas were time correlated to the known inductive measurement features. The initial dielectric breakdown and later plasma pinch and subsequent disruption (i.e. the source of the propulsion) were identified to be the principal phenomena to be detected. Signal parameter correlations between the inductive sensor and the antennas showed that the pinch phenomena can be correlated with the antenna signals. Good correlation results were obtained with the monopole antenna when using peak value and signal energy parameter from the antenna transient. An improvement in the correlation results, for the helical and Vivaldi antennas, was obtained when calculating the frequency band energy. In this case, the Vivaldi antenna achieved the best results. The results of the monopole antenna make it an alternative remote sensor for plasma focus, but for the application of a miniaturized plasma focus as pulsed plasma thruster, the Vivaldi antenna is a more feasible design to replace the inductive diagnostic due to its compact design in comparison to the monopole.

Rotor Fault Diagnosis Based on Characteristic Frequency Band Energy Entropy and Support Vector Machine.

Rotor is a widely used and easily defected mechanical component. Thus, it is significant to develop effective techniques for rotor fault diagnosis. Fault signature extraction and state classification of the extracted signatures are two key steps for diagnosing rotor faults. To complete the accurate recognition of rotor states, a novel evaluation index named characteristic frequency band energy entropy (CFBEE) was proposed to extract the defective features of rotors, and support vector machine (SVM) was employed to automatically identify the rotor fault types. Specifically, the raw vibration signal of rotor was first analyzed by a joint time–frequency method based on improved singular spectrum decomposition (ISSD) and Hilbert transform (HT) to derive its time–frequency spectrum (TFS), which is named ISSD-HT TFS in this paper. Then, the CFBEE of the ISSD-HT TFS was calculated as the fault feature vector. Finally, SVM was used to complete the automatic identification of rotor faults. Simulated processing results indicate that ISSD improves the end effects of singular spectrum decomposition (SSD) and is superior to empirical mode decomposition (EMD) in extracting the sub-components of rotor vibration signal. The ISSD-HT TFS can more accurately reflect the time–frequency information compared to the EMD-HT TFS. Experimental verification demonstrates that the proposed method can accurately identify rotor defect types and outperform some other methods.

Damage Mechanism Evaluation of Large-Scale Concrete Structures Affected by Alkali-Silica Reaction Using Acoustic Emission

Alkali-silica reaction has caused damage to concrete structures, endangering structural serviceability and integrity. This is of concern in sensitive structures such as nuclear power plants. In this study, acoustic emission (AE) was employed as a structural health monitoring strategy in large-scale, reinforced concrete specimens affected by alkali-silica reaction with differing boundary conditions resembling the common conditions found in nuclear containments. An agglomerative hierarchical algorithm was utilized to classify the AE data based on energy-frequency based features. The AE signals were transferred into the frequency domain and the energies in several frequency bands were calculated and normalized to the total energy of signals. Principle component analysis was used to reduce feature redundancy. Then the selected principal components were considered as features in an input of the pattern recognition algorithm. The sensor located in the center of the confined specimen registered the largest portion of AE energy release, while in the unconfined specimen the energy is distributed more uniformly. This confirms the results of the volumetric strain, which shows that the expansion in the confined specimen is oriented along the thickness of the specimen.

Investigation on the propagation characteristics of PD-induced electromagnetic waves in an actual 110 kV power transformer and its simulation results

Partial discharge (PD) detection using the ultra-high frequency (UHF) method is a well-known advanced insulation diagnosis method in power transformers. Investigation on propagation characteristics of electromagnetic (EM) waves in the power transformer is required for significant improvement of detecting PD signal by UHF method. In this paper, a 110/35/10kV three-phase oil-filled transformer with complete internal structure was utilized to investigate the propagation characteristics of EM waves. The amplitude, cumulative energy and frequency band energy of EM waves in different positions were analyzed. As a comparison, a simulation model whose size is same as the experimental transformer, built by CST Microwave Studio was also performed. The results show that the amplitude and cumulative energy of EM waves have similar change law, that is, they decrease non-linearly and the attenuation rate slows down with increasing distance from the PD source. During the propagation of EM waves, the higher frequency bands suffer greater attenuation leading to the less energy ratio. Compared with experimental results, numerical simulation brings about adequately accurate values and shows the similar trend with experiment. The results serve as the essential guides to improve the sensitivity of UHF detection and efficiency of online monitoring by UHF detection, and as a notable concern to optimize the band width for high-frequency detection.

Hydraulic Stability Analysis of a Large Prototype Francis Turbine Based on Field Test

Long time field tests of a 200 MW prototype Francis turbine over its full range of operation were conducted. From the experimental data, the time domain and frequency domain characteristics of the pressure fluctuations in the Francis turbine at different operation conditions were analyzed. Furthermore, the reason for the amplitude increase of pressure fluctuations and the correlation between the vibration and the pressure fluctuation was studied by using a multidimensional frequency band energy ratio analysis method. Based on the above analysis, some hydraulic stability characteristics of the large prototype Francis turbine are found, and other results are also obtained.