Time-varying Speed Research Articles

Potential Effects of Statistical complexity measure in improving the fault diagnosis performance under speed fluctuations

Abstract Many research studies are performed in extracting non-stationary signal properties such as instantaneous frequency (IF) and envelope to diagnose machinery faults under time-varying speed conditions. But accurate IF extraction is difficult from such complex fluctuating signals to identify the faults efficiently. Therefore, this paper proposes the Statistical Complexity Measure (SCM) as a feature that evaluates the randomness and complexity without any additional requirement, providing an outstanding result in multi-component fault detection under speed fluctuations. Primarily, the influence of SCM is proved by ranking the features with the help of the Random Forest (RF) algorithm. The fault classification of individual signal characteristics confirms its ranking order as well. Then, it is targeted towards the efficacy of the signal fusion approach in multicomponent fault diagnosis. Focusing on the essence of SCM, it is merged with the other three signal properties based on the ranking technique to investigate which one of the feature compositions is more advantageous. The machine learning classifier, SVM-RBF, achieves 94.7%, which proves the potential effects of SCM features in the industrial rotational machinery component under variable speed operating conditions.

A novel sliding mixing graph contrastive domain adaptation method for fault diagnosis under time-varying speeds

A novel sliding mixing graph contrastive domain adaptation method for fault diagnosis under time-varying speeds

Real-time Bayesian axle load estimation and structural identification of railway bridges under train loads based on strain monitoring

Health monitoring of railway bridges under train loads is of importance for the assessment and maintenance of railway infrastructure. The existing dynamic methods for the estimation of axle loads of trains require the track irregularities that are difficult to be obtained. Additionally, as trains have multiple carriages with a large number of axle loads without knowing magnitudes and positions, the corresponding estimation problem is essentially ill-conditioned. Furthermore, only the estimation of train loads is considered in the existing methods. The ill-conditioning problem may further deteriorate when the structural identification of railway bridges is taken into account. To address these problems, a Bayesian probabilistic approach for the real-time simultaneous estimation of train loads and structural parameters of railway bridges is developed using only strain measurements. From the train-track-bridge interaction dynamics, the axle loads of trains are modelled as modulated filtered noises, which avoids the direct analysis of the coupled system and thus does not require the additional information of track irregularities. Additionally, the time-varying speed parameter is introduced for the position tracking of axles, which allows the axle detection for the train loads with variable speeds. Furthermore, in order to tackle the ill-conditioned estimation problem, the prior information on axle loads from standardized trains is incorporated into the extended Kalman filter (EKF) to reduce the number of unknowns and improve the estimation. Examples for the estimation of a single-span bridge and a multi-span bridge under train loads are presented to illustrate the feasibility of the proposed methods.

A Motor Current Signal-Based Fault Diagnosis Method for Harmonic Drive of Industrial Robot under Time-Varying Speed Conditions

A Motor Current Signal-Based Fault Diagnosis Method for Harmonic Drive of Industrial Robot under Time-Varying Speed Conditions

Multi-Band Torsional Vibration Amplitude Demodulation for Planetary Gearbox Fault Diagnosis Under Time-Varying Speed Conditions

Multi-Band Torsional Vibration Amplitude Demodulation for Planetary Gearbox Fault Diagnosis Under Time-Varying Speed Conditions

I-METANET: Macroscopic Freeway Model for Real-Time Incident Impact Estimation and Traffic Management

Macroscopic traffic flow models, notably METANET, have been instrumental in the real-time estimation and optimization of control strategies for recurrent traffic congestions owing to their computational efficiency and robust performance. Their embedded formulations, however, are not best tailored to capture the traffic dynamics during roadway lane blockage owing to incidents where rapid and surging queue waves often propagate to upstream segments at time-varying but much faster speeds than under recurrent traffic congestion. Therefore, this study introduces I-METANET for use in incident traffic modeling and management, grounded mainly in METANET’s core mechanism but with the following enhancements: (1) reflecting incident-induced merging behaviors and their significant but attenuate impacts on the speed propagation over upstream segments; (2) incorporating the concurrent impacts of the approaching upstream traffic flows and the downstream incident-incurred queue waves on a subject segment’s evolving speed; and (3) integrating the compound effects of ramp-flows’ weaving maneuvers and the presence of incident queues on an interchange segment’s traffic conditions. The proposed I-METANET, calibrated and evaluated by the field data from Interstate 4 (I-4) in Florida, has demonstrated its expected improvements over METANET under incident scenarios, based on the produced time-varying speeds and flow rates over each roadway segment during incident clearance periods. An extensive sensitivity analysis with respect to key parameters in I-METANET has also been conducted, and evaluation results have confirmed the robustness of I-METANET for simulating freeway traffic dynamics during incident-incurred traffic scenarios.

Successive variational nonstationary mode decomposition for bearing multi-fault diagnosis undertime-varying speed conditions

Rolling bearings are important components in mechanical machinery, and their failure will directly affect the normal operation of the machine. Therefore, the analysis of mechanical machinery vibration signals is crucial for ensuring the normal operation of the machinery. Successive variational mode decomposition (SVMD) is an important technique for decomposing a bearing stationary signal into its characteristic modes with a priori penalty factor. Therefore, it cannot handle nonstationary bearing signals. To tackle the above problems, a novel method, named successive variational nonstationary mode decomposition (SVNMD), is developed in this article. First, a new decomposition framework is proposed by adopting the constructed resampling operator to modify the optimization function of the SVMD, which eliminates the influence of frequency mixing. Second, in order to automatically determine the optimal penalty factor, an iterative selection scheme is developed, which is free from prior knowledge. Third, an instantaneous frequency estimation theory is proposed to obtain the common trend function of the signal. Finally, a time-frequency representation with high-energy concentration is obtained to accurately identify the fault characteristics of rolling bearings. Both the simulation and experimental verification have confirmed the productiveness of the SVNMD in diagnosing multiple faults of bearings under time-varying speed conditions.

Transient lubrication of floating bush coupled with dynamics and kinematics of cam-roller in fuel supply mechanism of diesel engine

The roller-floating bush pin is a major component in the fuel supply mechanism of a diesel engine, and its tribological performance seriously affects the reliability of the equipment. In this study, a transient thermoelastic hydrodynamic lubrication analysis model of the double-layer oil film on the roller-floating bush pin is established, which is coupled with the dynamic and kinematic analysis model of the cam-roller. The transient dynamic characteristics, such as time-varying speeds and loads of the roller during the operation of the cam, were considered. Also, the elastic deformation and the thermal effect were considered in the lubrication model. The relevant experimental platform for this double-layer oil film lubrication structure was built, and the simulation model was verified through experiments. The influence of the operating conditions, including cam speed and plunger fuel pressure, on the lubrication performance of the structure is explored, and besides, the influence of the structural parameters, including length-diameter ratios and clearance ratios, is also analyzed. It shows the lubrication condition of the inner oil film is worse compared with the outer oil film. As the explored factors change, the pressure of the inner oil film is more affected than that of the outer oil film. This work could provide useful guidance in designing the roller-floating bush pin in the fuel supply mechanism.

Fault diagnosis of rolling bearings driven by multi-channel data fusion and feature fusion under time-varying speed conditions

Abstract Bearings, as the core component for power transmission, are crucial in ensuring the safe and reliable operation of equipment. However, the fault information contained in a single-channel vibration signal is inherently limited. Additionally, under time-varying speed conditions, features are prone to drift, and the cross-domain diagnostic performance of most traditional domain adaptation (DA) models may drop dramatically. To solve the above problems and enhance the ability of DA models in extracting domain invariant features, this paper introduces a Multi-channel data fusion and Attention-guided Multi-feature Fusion-driven Center-aligned Network (MAMC). Initially, a multi-channel time-frequency information fusion strategy based on wavelet transform is constructed to achieve a comprehensive fusion of multi-channel data, thereby obtaining richer fault feature representations. Subsequently, a multi-branch feature fusion network, integrated with an attention mechanism, is devised to capture significant features across various dimensions and scales, resulting in more comprehensive and representative fault features. Finally, a novel Center-Aligned Domain Adaptation method (CADA) is proposed based on domain adversarial methods and center loss. By minimizing the distance between deep domain invariant features and trainable common class centers, the issue of domain shift between data is effectively alleviated, and the cross-domain diagnostic performance of DA models under the time-varying speed conditions is improved. The experimental results indicate that the MAMC method exhibits superior performance on both bearing datasets and is a promising approach for cross-domain intelligent fault diagnosis.

Unbalance detection of threshing cylinder in non-stationary rotation based on variational mode extraction

The rotating speed of the threshing cylinder was hardly constant in practice due to transmission error and cylinder speed would change with feed quantity to ensure the grain threshing effect. This paper proposed a novel unbalance detection method for non-stationary rotation. Variational mode extraction (VME) was introduced to extract unbalanced vibration signal and the unbalance feature index (UFI) was constructed as a criterion of weighting factor selection. Moreover, the unbalance phase calculation method and phase reference in non-stationary were proposed, which could eliminate the influence of speed change. The advantages of VME and UFI were verified by simulation, and the effectiveness of proposed method was proved by experiment on multi-cylinder test rig and combine harvest. This method could realize the reconstruction of unbalanced vibration signal in time-varying speed with low computational cost, break the strict requirement of constant speed of the existing balancing method.

Rolling element bearing fault diagnosis under time-varying speed conditions based on improved wavelet packet transform and envelope order tracking

Abstract Rolling element bearings (REBs) are crucial components of rotating machinery and play an important role in industrial production. Failure of REBs can lead to significant economic losses and catastrophic accidents. In order to ensure their safe and stable operation, we propose a novel approach called EOT-IWPT which combines the envelope order tracking (EOT) with an improved wavelet packet transform algorithm (IWPT) for diagnosing failures in REBs under time-varying speed conditions. First, the time- domain vibration signal is resampled at constant angle intervels according to the phase of the rotating shaft to obtain the angle-domian signal. Next, the resampled signal is decomposed to sub-band signals using IWPT. Third, the squared envelope spectra (also called envelope order spectra) of the last layer wavelet packet coefficients are obtained by the Hilbert transform and the Fourier transform. Finally, the diagnostic results can be easily determined based on the squared envelope spectra. The procedure and performance of the proposed approach are illustrated and evaluated through simulation analysis, proving that the proposed method can effectively extract the fault features. Furthermore, experimental analysis results indicate that the proposed method outperforms both EOT-WPT and SK-EOT-BPF.

Time-varying speed fault diagnosis based on dual-channel parallel multi-scale information

Time-varying speed fault diagnosis based on dual-channel parallel multi-scale information

Uncertainty-driven dynamic ensemble framework for rotating machinery fault diagnosis under time-varying working conditions

Multi-scale ensemble learning combines different scales of feature resolution, thereby improving fault diagnostic accuracy. However, the effectiveness of different information scales in characterizing fault features under time-varying speed conditions varies with speed. It is difficult for existing ensemble strategies to ensure the effectiveness of feature information when ensemble multi-scale feature information is involved. Accordingly, we propose an uncertainty-driven dynamic ensemble Bayesian convolutional neural network (DEBCNN) framework. The uncertainty of the results of different scale models was used to dynamically determine their weights in the ensemble framework, which reduced the influence of irrelevant features on the diagnostic results. By employing the proposed dynamic ensemble strategy, the ensemble framework can utilize fault feature information corresponding to different rotational speeds in the final diagnostic results. Experiments on motor and bearing datasets illustrate the superiority of this strategy over other techniques. This study provides useful insights for further research in the field of fault diagnosis of rotating machinery at time-varying speeds.

Adaptive extraction of characteristic ridges from time-frequency representation for wheelset bearings failure diagnosis under time-varying speed

Instantaneous frequencies-based order tracking is an excellent approach for bearing faults diagnosing at time-varying speeds. The key is to extract ridges from time-frequency representation. However, their precision seriously relies on the cost function and bandwidth selection. To address these issues, an adaptive extraction of characteristic ridges (AECR) method that integrates a variable-bandwidth ridge edge detection (VBRED) and an adaptive exponential cost function (AECF) is presented. VBRED provides a variable-bandwidth search area-based clustering algorithm to isolate neighboring components. AECF can be automatically adjusted based on the signal signature to ensure the smoothness of the ridge. The average frequency ratios of the extracted ridges are used to identify the health conditions of the bearings. Finally, the effectiveness of the proposed AECR is validated by simulations and experiments. The results prove that it performs excellently when the energy and relative position of the ridges are close, compared to other state-of-the-art methods.

Steering authority allocation strategy for human-machine shared control based on driver take-over feasibility

Misoperation when a driver is distracted, e.g., turning the steering wheel unconsciously, preventing the machine from turning the steering wheel, etc., can lead to a false take-over of the vehicle, which poses a danger to shared control. Therefore, it is essential to evaluate the feasibility of driver take-over in distraction status. This paper proposes a steering authority allocation strategy based on driver take-over feasibility to reduce the interference of distracted driver behavior on shared control. First, a driver take-over feasibility framework is established based on quantified driver distraction levels and take-over intention. Then the shared control system is established based on a linear parameter-varying vehicle model and non-cooperative game theory to suppress the interference of time-varying speed and parameter uncertainty on shared control. Subsequently, the steering authority allocation strategy is constructed in conjunction with driver take-over feasibility and environmental risk evaluation. The proposed sigmoid-based mapping function ensures fast and smooth authority allocation. Finally, a series of driver-in-the-loop experiments are conducted to validate the effectiveness of our proposed strategy across various driving scenarios, and the results demonstrate that our strategy has superior performance than other advanced shared control schemes in reducing steering burden, improving driving safety, and enhancing vehicle stability, etc.

Fault diagnosis of rolling bearing under complex working conditions based on time-frequency joint feature extraction-deep learning

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

Hedging irrigated maize crop yields using temperature derivatives in Malawi

Agriculture production yield varies with weather changes. This causes farmers incur loses. For instance, extreme temperature leads to low maize yield. This study describes incomplete temperature weather derivatives in agriculture markets and applies risk management hedging techniques. It focuses on hedging crop yield against extreme temperatures during irrigation, farming which is done without greenhouse. This study's primary goal is to hedge irrigated maize crop yields using temperature derivatives. This is achieved firstly by modelling a daily average temperature stochastical model. Then, deriving statistical properties of the model based on 1990–2020 Kasungu District Temperature historical data. Lastly, pricing temperature derivatives to hedge maize crop yield. This study uses 1990–2020 Kasungu District Temperature historical data. Then a stochastically Ornstein-Uhlenbeck process with the time-varying speed of reversion, seasonal mean, and local volatility that depends on the local average temperature was proposed. Based on the average temperature model, down and output barrier option pricing models for average temperature and growing degree day (GDD) are applied. The study results shows that when the GDD is above the barrier level the barrier option does not knock out. Hence gives the holder the right to buy the underlying asset since it does not reach or fall below a predefined level over the option's lifetime. If the GDD does not exceed the barrier level, then the farmer will not have to pay the premium because the option is invalid. On the same note, the farmer will have to exercise his right by paying the premium of calculated premium when the GDD exceeds the barrier level. In return for this, the farmer gets paid off which happens to be the difference between GDD and barrier multiplied $1. In line with Malawi's 2063 Millennium Development Goals (MDGs), this study acts as an eye opener for the government to put a policy on whether derivatives should be practised in our country hence, increase cash holding by improving the situation of the farmer and country.

Research on Heave Compensation System Based on Switched Reluctance Motor

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme.

Decentralized learning control for high-speed trains with unknown time-varying speed delays

Treating the multi-point-mass dynamic model of high-speed trains as an interconnected system, this study proposes a decentralized iterative learning control scheme for high-speed trains to achieve the trajectory tracking goal. By making reasonable estimates of the interaction term and compensating for it, the proposed control scheme utilizes only local information from each carriage and does not need any inter-carriage information exchange. The zero-error tracking of the desired trajectory is guaranteed even in a restricted communication environment. Considering unknown time-varying speed delays in the actual high-speed train operations, a modified decentralized iterative learning control scheme is also provided to address the negative impact of speed delays. The convergence of tracking errors is strictly proven by constructing appropriate composite energy functions. Numerical simulations further verify the theoretical results.

Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples

The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance of existing models on single-source domain small sample data, a novel single-source domain generalization fault diagnosis (SDGFD) framework, the prior knowledge embedded convolutional autoencoder (PKECA), is proposed. During the training phase, first, single-source domain data are used to construct prior features based on the time domain, frequency domain, and time-frequency domain. Second, a prior knowledge embedding structure based on the convolutional autoencoder is built, which compresses the prior knowledge and original vibration data into a high-dimensional space of consistent dimensions, embedding the prior knowledge into the features corresponding to the vibration data using a mean squared error loss function. Subsequently, the proposed centroid-based self-supervised learning (CBSSL) strategy further constrains high-dimensional features, improving the generalization ability. The designed sparse regularized activation (SRA) function significantly enhances the regularization effect on features. During the testing phase, it is only necessary to input the data from the unknown domain to identify the fault types. The experimental results show that the proposed method achieves superior performance in fault diagnosis tasks involving cross-speed, time-varying speed, and small sample data in SDGFD, demonstrating that PKECA has strong generalizability. The code can be found here: https://github.com/John-520/PKECA. © 2024 Elsevier Science. All rights reserved