Time-domain Statistical Analysis Research Articles

Numerical study on dynamic analysis of a nine-module floating aquaculture platform under irregular waves

A numerical study of the dynamics of a nine-module floating aquaculture platform in irregular waves is proposed, to examine the effect of the connector type between net cages and incident wave direction on the hydrodynamic response. A numerical model based on the coupled boundary element method and lumped-mass model was used to simulate the dynamic behavior of the aquaculture platform. Time-domain statistical analysis, and time-frequency analysis based on Hilbert-Huang transform on the motion responses of net cages and the mooring force responses of the platform were then carried out. The results indicate that the mooring system has a decisive influence on the frequency-domain characteristics of surge motion, while the influence of connector can be ignored. The surge motion under flexible hawser connector is less sensitive to wave incident direction, while the wave direction has a greater impact on surge motion under spherical joint connector. The amplitudes of pitch motion under two different connectors decrease significantly under oblique incident waves, compared with perpendicular incident wave condition. The wave incident direction has little effect on the overall mooring force response of the platform. The mooring force responses at different positions show completely different frequency characteristics when the cages are connected by spherical joints. The larger the pitch response of the cage, the smaller the mooring force response of the mooring chain connected to the net cage. There is a positive correlation between the gravity frequency of the mooring force response and the gravity frequency of the pitch response of the net cage.

Experimental Study of the Hydrodynamic Characteristics of a Submerged Floating Tunnel under Freak Wave (II: Time-Frequency Domain Study)

A freak wave is a spike in a random wave series and hence the local characteristics in the time-domain are of key importance. When freak waves act on moored floating structures, the dynamic responses of the structures in the time and frequency domains change interdependently in a short period of time. It is difficult to comprehensively and accurately describe this physical process using a single-dimensional analysis method, such as time-domain statistical analysis or frequency-domain spectral analysis. The wavelet analysis method, which can simultaneously provide the time-domain and frequency-domain joint information of the physical process, is used to discuss the time-frequency joint variation characteristics of the dynamic responses of a two-dimensional submerged floating tunnel under a freak wave. The time-frequency characteristics of the dynamic responses induced by the freak wave and the differences from the action under random waves are investigated, with a particular emphasis on the ‘convex variation’ characteristics of the dynamic responses under a freak wave. The results show that: (1) The wavelet analysis method can effectively describe the basic characteristics of the dynamic responses of the SFT under a freak wave and clearly distinguish the differences in dynamic responses under freak and random waves. (2) Freak waves have dynamic amplification effects, which are related to the freak wave parameter α1, on a two-dimensional SFT. Following the action of freak waves on a two-dimensional SFT, significant energy concentration occurs in the time-frequency spectrum of the dynamic response in a certain time and frequency range. The degree of energy concentration increases nonlinearly with an increase in α1, and a certain high-frequency energy appears in the time-frequency spectrum of the motion response. The maximum values of the time-frequency spectra of the dynamic responses under a freak wave are much larger than those under a random wave with the identical wave spectrum. (3) Following the action of a freak wave on a two-dimensional SFT, the generalised energy spectra of surge, heave, pitch, and mooring tensions have convex peak values, which occur simultaneous with the occurrence of the freak wave, and the convex parts significantly increase as α1. (4) The time lengths of the influence of a freak wave on the dynamic responses exceeded the freak wave period. With an increase in α1, the time ranges of the large values of the time-frequency spectra of surge, heave, pitch, and mooring tensions increase nearly linearly.

Knock-Prediction System for Kerosene Engines Using In-Cylinder Pressure Signal

Piston engines fueled by kerosene have a strong application prospect in special vehicles and small aircrafts, but the low amount of octane in kerosene fuel causes its knock combustion phenomenon to be particularly serious. A knock will deteriorate the power and economy of the engine and will damage the engine in serious cases. Therefore, knocking is the key problem with kerosene engines. We propose a knock-prediction system for kerosene engines based on in-cylinder pressure signals. Firstly, the intrinsic mode function (IMF) caused by knock resonance is extracted from the in-cylinder pressure signal via empirical mode decomposition (EMD) and a time–frequency domain analysis. A time-domain statistical analysis (TDSA) combined with a principal component analysis (PCA) is used to extract features from the IMF. Finally, the data collected from the test bench are trained by a support vector machine to obtain the knock-prediction model. Compared with other technical combinations for training, the proposed scheme achieved more accurate results in knock prediction. Considering the working characteristics of kerosene engines, a slight knock can increase the power of a kerosene engine. Therefore, some incorrectly predicted cycles (slight-knock cycles) do not affect the normal operation of the engine.

An Autoencoder based Emotional Stress State Detection Approach by using Electroencephalography Signals

Identifying hazards from human error is critical for industrial safety since dangerous and reckless industrial worker actions, as well as a lack of measures, are directly accountable for human-caused problems. Lack of sleep, poor nutrition, physical deformities, and weariness are some of the key factors that contribute to these risky and reckless behaviors that might put a person in a perilous scenario. This scenario causes discomfort, worry, despair, cardiovascular disease, a rapid heart rate, and a slew of other undesirable outcomes. As a result, it would be advantageous to recognize people's mental states in the future in order to provide better care for them. Researchers have been studying electroencephalogram (EEG) signals to determine a person's stress level at work in recent years. A full feature analysis from domains is necessary to develop a successful machine learning model using electroencephalogram (EEG) inputs. By analyzing EEG data, a time-frequency based hybrid bag of features is designed in this research to determine human stress dependent on their sex. This collection of characteristics includes features from two types of assessments: time-domain statistical analysis and frequency-domain wavelet-based feature assessment. The suggested two layered autoencoder based neural networks (AENN) are then used to identify the stress level using a hybrid bag of features. The experiment uses the DEAP dataset, which is freely available. The proposed method has a male accuracy of 77.09% and a female accuracy of 80.93%.

Particle collision behavior and heat transfer performance in a Na2SO4 circulating fluidized bed evaporator

• Time-domain statistical analysis is used to investigate particle collision behavior. • Polytetrafluoroethylene particles can enhance the heat transfer of Na 2 SO 4 solution. • Effect of operating variables on collision intensity and heat transfer are studied. • Standard deviation and enhancement factor increase with amount of added particles. • Particle collision behavior and heat transfer in Na 2 SO 4 and H 2 O are compared. The particle collision behavior and heat transfer performance are investigated to reveal the heat transfer enhancement and fouling prevention mechanism in a Na 2 SO 4 circulating fluidized bed evaporator. The particle collision signals are analyzed with standard deviation by varying the amount of added particles ε (1%–3%), circulation flow velocity u (0.37–1.78 m·s −1 ), and heat flux q (7.29–12.14 kW·m −2 ). The results show that the enhancement factor reach up to 14.6% by adding polytetrafluoroethylene particles at ε = 3%, u = 1.78 m·s −1 , and q = 7.29 kW·m −2 . Both the standard deviation of the particle collision signal and enhancement factor increase with the increase in the amount of added particles. The standard deviation increases with the increase in circulation flow velocity; however, the enhancement factor initially decreases and then increases. The standard deviation slightly decreases with the increase in heat flux at low circulation flow velocity, but initially increases and then decreases at high circulation flow velocity. The enhancement factor decreases with the increase in heat flux. The enhancement factor in Na 2 SO 4 solution is superior to that in water at high amount of added particles. The empirical correlation for heat transfer is established, and the model results agree well with the experimental data.

In-Situ Test Method for Hydrodynamic Characteristics of Water Flowing Around Piles

The dynamic characteristics of the flow around the pile directly determine the distribution law of the force of the flow on the pile, which is the basis of the calculation of the flow load of the hydraulic structure. At present, the model test and numerical simulation methods are mainly used to study the flow characteristics around the pile. There are problems of model scale effect and test accuracy in the laboratory model test, and inherent problems in the numerical simulation method, such as the determination of medium parameters, mesh division, and boundary conditions. Therefore, the research results cannot reflect the actual situation of the interaction between water flow and pile. This paper developed a field test system for measuring the hydrodynamic pressures on surface of piles which are the vital foundations of wharves under superhigh Reynolds number. And the analysis methods of in-situ data, which can realize the multifunction including calibration and transformation for test data, frequency and time-domain statistical analysis process, were put forward according to the theory of stationary random process and mathematical statistics method. A representative frame wharf located in the upper reaches of the Yangtze River was performed as a case study, and the developed system was utilized to operate the in-situ test on this practice. Application showed that the field test system and the analysis methods can be applied to obtain the more accurate distribution of the pile surface hydrodynamic pressure. And the drag coefficient in wharf field ranged from 0.30 to 0.40. These findings in this paper may provide significant technical support for the in-situ test in similar structures and some reference for drag force calculation of flow around pile in the engineering design.

A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine

Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect. Collecting engine vibration signals from an engine cylinder block is an effective way to detect engine knock. This paper proposes an intelligent engine knock detection system based on engine vibration signals. First, filtered signals are obtained by utilizing variational mode decomposition (VMD), which decomposes the original time domain signals into a series of intrinsic mode functions (IMFs). Moreover, the values of the balancing parameter and the number of IMF modes are optimized using genetic algorithm (GA). IMFs with sample entropy higher than the mean are then selected as sensitive subcomponents for signal reconstruction and subsequently removed. A multiple feature learning approach that considers time domain statistical analysis (TDSA), multi-fractal detrended fluctuation analysis (MFDFA) and alpha stable distribution (ASD) simultaneously, is utilized to extract features from the denoised signals. Finally, the extracted features are trained by sparse Bayesian extreme learning machine (SBELM) to overcome the sensitivity of hyperparameters in conventional machine learning algorithms. A test rig is designed to collect the raw engine data. Compared with other technology combinations, the optimal scheme from signal processing to feature classification is obtained, and the classification accuracy of the proposed integrated engine knock detection method can achieve 98.27%. We successfully propose and test a universal intelligence solution for the detection task.

Machine learning based health assessment model for high pressure output pumps in LNG terminals

High pressure output pumps are one of the critical equipment in LNG terminals. Since the health condition of high pressure output pumps has a direct influence on production capability of the terminal, health assessment for these pumps in real time plays an important role on guaranteeing efficient productivity of LNG terminals. Using condition monitoring data, a machine learning based health assessment model for high pressure output pumps is proposed. Health features are constructed based on time domain statistical analysis and wavelet packet decomposition, and a SVR model is trained to calculate a health index from extracted features. Actual operating data in Qingdao LNG terminal are used for model validation. Results show that the calculated health indices are sensitive to faults and anomalies of the pumps, and are good indicators of pump health status. The proposed model also shows capability of early warning for some sudden failures, which can be valuable in the operation and maintenance management of LNG terminal equipment.

A novel optimized multi-kernel relevance vector machine with selected sensitive features and its application in early fault diagnosis for rolling bearings

Since the vibration signal of mechanical equipment with early faults is highly similar to that of mechanical equipment under the normal state, it is still a great challenge to extract sensitive features from the original vibration signal to execute the intelligent fault diagnosis for mechanical equipment. An early fault diagnosis method for rolling bearings based on multi-kernel relevance vector machine and multi-domain features was proposed in this paper. In order to reflect the time-varying characteristics, the vibration signals and operation status of rolling bearings in time series were incorporated into the process of early fault diagnosis. The three steps of the early fault diagnosis method for rolling bearings were as follows. Firstly, the vibration signals of rolling bearings during operation were measured online. Secondly, the original vibration signals were decomposed by wavelet packet transformation. The fault features were extracted from sensitive frequency band by time domain statistical analysis as well as, frequency domain statistical analysis, and then the multi-domain feature set was constructed to fully characterize the intrinsic properties of vibration signals. The correlation analysis was adopted to eliminate insensitive features from original multi-domain feature set. The low-dimensional feature set that are highly sensitive to early failures was reconstructed to improve the computational efficiency for subsequent fault diagnosis. Finally, the intelligent fault diagnosis was carried out based on the multi-kernel relevance vector machine model. The performance of this proposed method has been validated in practical rolling bearing fault diagnosis. The results show that the proposed method can achieve higher diagnosis accuracy for rolling bearing under different working conditions than traditional single-kernel model and is effective in early fault diagnosis.

Multisensory Data-Driven Health Degradation Monitoring of Machining Tools by Generalized Multiclass Support Vector Machine

Health degradation monitoring of machining tools is of great importance in industrial application field. In this paper, a novel multisensory data-driven health degradation monitoring system schema for the machining tools is proposed by using a generalized multiclass support vector machine (GenSVM). In this schema, multidimensional feature extraction is implemented in the time domain, frequency domain, and time-frequency domain based on the time domain statistical analysis, power spectrum analysis, and complete ensemble empirical mode decomposition with adaptive noise, respectively. On the basis, effective features that are strongly associated with the degradation process are picked out using a Pearson correlation coefficient. Meanwhile, a new and flexible GenSVM model is constructed to identify the health state of machining tools, which integrates a simplex encoding and iterative majorization optimization algorithm. A practical application case study is implemented to verify the effectiveness of the proposed method. The results show the superior performance of the proposed method compared with other standard methods.

Non-intrusive characterization of sand particles dispersed in gas–water bubbly flow using straight and bent pipes with vibration sensing

The transport of a solid dispersed in a gas-water bubbly pipe flow is widely encountered in petroleum engineering. In this paper, the characterization of solid particles dispersed in a water-gas bubbly flow was developed based on vibration sensing from straight and bent pipes. Typical frequency-domain analysis, time-domain statistical analysis, and time-domain joint analysis were applied for sand-vibration signal identification and characterization. Verification experiments were performed, and good agreements were found between the sand concentration (0–0.24 wt%) with uniformly mixed sand size (300–600 μm) and sand-vibration energy in water flow and water-gas bubbly flow. Characteristic frequency bands of 4–5 kHz (horizontal straight pipe), 3.9–5 kHz (bent pipe), and 4.7–5 kHz (vertical straight pipe) were found. The sensor installed on a bent pipe obtained the highest vibration energy of water/water-gas bubbly flow among tested pipes; with the increase of sand injected, a sensor installed on the outer wall at 45° on the elbow obtained the most obvious vibration energy caused by sand–wall interaction. The validity of the detected sand signals was verified using an acoustic sensor. In summary, the vibration-sensing method is applicable to characterize solid particles in water-gas bubbly pipe flow.

Research of Springing and Whipping Influence on Ultra-Large Containerships’ Fatigue Analysis

Rules of Classification Societies all around the world have made changes on design wave loads’ value and fatigue influence factor modification due to the influence of springing and whipping on ultra-large containerships. The paper firstly introduced 3-D linear hydroelastic theory in frequency domain and 3-D nonlinear hydroelastic theory in time domain, considering large amplitude motion nonlinearity and slamming force due to the severe relative motion between ship hull and wave. Then the spectrum analysis method and time domain statistical analysis method were introduced, which can make fatigue analysis under a series of standard steps in frequency and time domain, respectively. Finally, discussions on the influence factor of springing and whipping on fatigue damages of 8500TEU and 10000TEU containerships with different loading states were made. The fatigue assessment of different position on the midship section was done on the basis of nominal stress. The fatigue damage due to whipping can be the same as the fatigue damage due to springing and even sometimes can be larger than the springing damage. Besides, some suggestions on calculating load case selection were made to minimize the quantity of work in frequency and time domain. Thus, tools for fatigue influence factor modification were provided to meet the demand of IACS-UR.

State Inspection and Fault Diagnosis System of Train Axel Box Bearing Based on Virtual Instrument

Fault diagnosis of train bearing is an important method to ensure the security of railway. In the paper, four time domain statistical analysis methods and resonance demodulation method as a frequency domain analysis method are introduced. A state inspection and fault diagnosis system of train axel box bearing is constructed and its efficiency is also tested by wheel set experiment.

Regularity and Matching Pursuit feature extraction for the detection of epileptic seizures

BackgroundThe neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. MethodsIn this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. ResultsSeveral versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. Comparison with existing methods: A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. ConclusionsThe experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.

Computer methods for switched circuits

This paper presents a state-of-the-art review of the recent advances in computer methods for analysis and design of switched circuits in both time and frequency domains. It also presents new research results on time-domain analysis and sensitivity of switched nonlinear circuits, time- and frequency-domain statistical analysis of switched linear and nonlinear circuits, and efficient modeling and analysis of clock feedthrough and charge injection in switched linear circuits. In time-domain analysis, the modeling of switches and its effect on the simulation of switched circuits are investigated. Formulation methods for these circuits are examined. Inconsistent initial conditions arising from ideal switching are investigated and numerical methods that derive the consistent initial conditions are examined. Sampled-data simulation (SDSIM) of switched linear circuits including clocked sigma-delta modulators is investigated. SDSIM is extended to switched nonlinear circuits. Time-domain sensitivity of switched linear and nonlinear circuits is analyzed using SDSIM. Efficient time-domain statistical analysis of switched linear and nonlinear circuits is introduced, and their effectiveness is assessed using Monte Carlo simulation. Methods that compute the effect of the clock jitter of periodically switched linear (PSL) circuits are examined. Time-domain noise analysis of PSL circuits is investigated. In frequency-domain analysis, exact frequency analysis of multiphase PSL circuits is reviewed. Sensitivity analysis of these circuits is examined in detail. Adjoint network theory and its usefulness in noise and sensitivity analysis of switched linear circuits are studied. Group delay of PSL circuits is investigated briefly. Efficient modeling and analysis of clock feed-though and charge injection of PSL circuits are introduced. Distortion and sensitivity of periodically switched nonlinear circuits with mild nonlinearities are investigated. Finally, frequency-domain noise analysis of PSL circuits is examined.

Diagnosis of the Tapping Process by Information Measure and Probability Voting Approach

A new method for the diagnosis of the tapping process using the information measure and multiple probability voting scheme is proposed. Considering the features of short cutting-duration and the large uncertainties existing in a tapping process, a set of indices based on the time-domain statistical analysis has been formed. These indices have then been evaluated and ranked using an algorithm that calculates the information gain of each index about the tapping process. The final classification decision can be made by a voting scheme based on the conditional probability functions for multiple indices. Furthermore, the information gains estimated in the index evolution process can be used as a weighting function during voting to improve efficiency and reliability. From a tapping test, which includes five different tapping conditions, a success rate of 95 percent has been achieved.

A zero-crossing consistency method for formant tracking of voiced speech in high noise levels

A technique is described for the determination of the first three formant frequencies of a voiced speech signal corrupted by high levels of noise. The technique involves a rough frequency-domain analysis followed by an accurate time-domain statistical analysis. The frequency-domain analysis is performed with a bank of overlapped, shifted, bandpass digital filters chosen to cover the frequency range of the first three formant frequencies. The time-domain analysis involves a calculation of the consistency of the intervals between successive zero-crossings at the output of each filter. As the algorithm proceeds, those filters which contain peaks in the energy spectrum are chosen as a first estimate of the location of the first three formants. The final choice of filter containing each formant is made, in the neighborhood of the first estimate, by choosing that filter with a signal output having the maximum zero-crossing interval consistency. A decision procedure is applied to several calculated parameters to result in a measurement of each of the first three formant frequencies. The results of extensive testing of the method are reported for speech contaminated by high levels of additive white Gaussian noise.