Rotor Speed Signal Research Articles

Broadening the application of angle-time cyclostationary analysis in bearing fault diagnosis using precise speed reconstruction

Angle-time cyclostationary (AT-CS) analysis has developed into the most powerful analytical tool for studying vibration signals of rotating machinery under variable operating conditions. However, due to limitations in the accuracy of traditional rotational speed estimation algorithms, the effectiveness of AT-CS analysis may be compromised in the absence of robust keyphasor signal. To address this challenge, a novel rotational speed signal estimation method based on “double estimation” strategy is proposed to expand the application scenarios of AT-CS. This methodology entails an initial estimation of the rough speed through time-frequency analysis, followed by refinement using phase demodulation techniques. Additionally, the Vold–Kalman order tracking method is employed to isolate the single frequency signal component essential for phase demodulation techniques. In situations where the spectrum is complex and the speed fluctuates violently, this method can still accurately obtain the speed signal, achieving similar effects to the rotational velocity transducer. The complex speed curve experiment and turbine pump bearing experiment with severe speed fluctuation are carried out to verify the algorithm.

A New Method of Intelligent Fault Diagnosis of Ship Dual-Fuel Engine Based on Instantaneous Rotational Speed

Ship engine misfire faults not only pose a serious threat to the safe operation of ships but may also cause major safety accidents or even lead to ship paralysis, which brings huge economic losses. Most traditional fault diagnosis methods rely on manual experience, with limited feature extraction capability, low diagnostic accuracy, and poor adaptability, which make it difficult to meet the demand for high-precision diagnosis. To this end, a fusion intelligent diagnostic model—ResNet–BiLSTM—is proposed based on a residual neural network (ResNet) and a bidirectional long short-term memory network (BiLSTM). Firstly, a multi-scale decomposition of the instantaneous rotational speed signal of a ship’s engine is carried out by using the continuous wavelet transform (CWT), and features containing misfire fault information are extracted. Subsequently, the extracted features are fed into the ResNet–BiLSTM model for learning. Finally, the intelligent diagnosis of ship dual-fuel engine misfire faults is realized by the classifier. The model combines the advantages of ResNet18 in image feature extraction and the capability of BiLSTM in temporal information processing, which can efficiently capture the time-frequency features and dynamic changes in the fault signal. Through comparison experiments with fusion models AlexNet–BiLSTM, VGG–BiLSTM, and the existing AlexNet–LSTM and VGG–LSTM models, the results show that the ResNet–BiLSTM model outperforms the other models in terms of diagnostic accuracy, robustness, and generalization ability. This model provides an effective new method for intelligent diagnosis of ship dual-fuel engine misfire faults to solve the traditional diagnostic methods’ limitations.

Improved Performance of the Permanent Magnet Synchronous Motor Sensorless Control System Based on Direct Torque Control Strategy and Sliding Mode Control Using Fractional Order and Fractal Dimension Calculus

This article starts from the premise that one of the global control strategies of the Permanent Magnet Synchronous Motor (PMSM), namely the Direct Torque Control (DTC) control strategy, is characterized by the fact that the internal flux and torque control loop usually uses ON–OFF controllers with hysteresis, which offer easy implementation and very short response times, but the oscillations introduced by them must be cancelled by the external speed loop controller. Typically, this is a PI speed controller, whose performance is good around global operating points and for relatively small variations in external parameters and disturbances, caused in particular by load torque variation. Exploiting the advantages of the DTC strategy, this article presents a way to improve the performance of the sensorless control system (SCS) of the PMSM using the Proportional Integrator (PI), PI Equilibrium Optimizer Algorithm (EOA), Fractional Order (FO) PI, Tilt Integral Derivative (TID) and FO Lead–Lag under constant flux conditions. Sliding Mode Control (SMC) and FOSMC are proposed under conditions where the flux is variable. The performance indicators of the control system are the usual ones: response time, settling time, overshoot, steady-state error and speed ripple, plus another one given by the fractal dimension (FD) of the PMSM rotor speed signal, and the hypothesis that the FD of the controlled signal is higher when the control system performs better is verified. The article also presents the basic equations of the PMSM, based on which the synthesis of integer and fractional controllers, the synthesis of an observer for estimating the PMSM rotor speed, electromagnetic torque and stator flux are presented. The comparison of the performance for the proposed control systems and the demonstration of the parametric robustness are performed by numerical simulations in Matlab/Simulink using Simscape Electrical and Fractional-Order Modelling and Control (FOMCON). Real-time control based on an embedded system using a TMS320F28379D controller demonstrates the good performance of the PMSM-SCS based on the DTC strategy in a complete Hardware-In-the-Loop (HIL) implementation.

Study on Mathematical Models for Precise Estimation of Tire–Road Friction Coefficient of Distributed Drive Electric Vehicles Based on Sensorless Control of the Permanent Magnet Synchronous Motor

In order to reduce the use of wheel angular velocity sensors and improve the estimation accuracy and robustness of the tire–road friction coefficient (TRFC) in non-Gaussian noise environments, this paper proposes a sensorless control-based distributed drive electric vehicle TRFC estimation algorithm using a permanent magnet synchronous motor (PMSM). The algorithm replaces the wheel angular velocity signal with the rotor speed signal obtained from the sensorless control of the PMSM. Firstly, a seven-degree-of-freedom vehicle dynamics model and a mathematical model of the PMSM are established, and the maximum correntropy singular value decomposition generalized high-degree cubature Kalman filter algorithm (MCSVDGHCKF) is derived. Secondly, a sensorless control system of a PMSM based on the MCSVDGHCKF algorithm is established to estimate the rotor speed and position of the PMSM, and its effectiveness is verified. Finally, the feasibility of the algorithm for TRFC estimation in non-Gaussian noise is demonstrated through simulation experiments, the Root Mean Square Error (RMSE) of TRFC estimates for the right front wheel and the left rear wheel were reduced by at least 41.36% and 40.63%, respectively. The results show that the MCSVDGHCKF has a higher accuracy and stronger robustness compared to the maximum correntropy high-degree cubature Kalman filter (MCHCKF), singular value decomposition generalized high-degree cubature Kalman filter (SVDGHCKF), and high-degree cubature Kalman filter (HCKF).

Tire-Road Friction Coefficient Estimation for Distributed Drive Electric Vehicles Using PMSM Sensorless Control

Many tire-road friction coefficient estimation methods require the wheel angular speed sensor to provide a signal. In this paper, a tire-road friction coefficient estimation algorithm for distributed drive electric vehicles using permanent magnet synchronous motors(PMSM) sensorless control is proposed to reduce the use of wheel angular speed sensors. The wheel angular speed signal is replaced by the rotor speed signal obtained from PMSM sensorless control. First, the three degrees of freedom vehicle dynamics model and the PMSM mathematical model are established, and a strong tracking cubature Kalman filtering algorithm based on orthogonal transformation (T-STCKF) is derived to overcome the problem that the STCKF algorithm is prone to nonlocal sampling when dealing with high dimensional systems. Second, a PMSM sensorless control system based on the adaptive exponent reaching law of sliding mode control and the T-STCKF algorithm is established, and confirmed its validity. Finally, the feasibility of the proposed algorithm is demonstrated through multicase simulation and experiment and the results shows that the T-STCKF algorithm is more accurate than the STCKF algorithm.

Improved Performance for PMSM Sensorless Control Based on Robust-Type Controller, ESO-Type Observer, Multiple Neural Networks, and RL-TD3 Agent.

This paper summarizes a robust controller based on the fact that, in the operation of a permanent magnet synchronous motor (PMSM), a number of disturbance factors naturally occur, among which both changes in internal parameters (e.g., stator resistance Rs and combined inertia of rotor and load J) and changes in load torque TL can be mentioned. In this way, the performance of the control system can be maintained over a relatively wide range of variation in the types of parameters mentioned above. It also presents the synthesis of robust control, the implementation in MATLAB/Simulink, and an improved version using a reinforcement learning twin-delayed deep deterministic policy gradient (RL-TD3) agent, working in tandem with the robust controller to achieve superior performance of the PMSM sensored control system. The comparison of the proposed control systems, in the case of sensored control versus the classical field oriented control (FOC) structure, based on classical PI-type controllers, is made both in terms of the usual response time and error speed ripple, but also in terms of the fractal dimension (DF) of the rotor speed signal, by verifying the hypothesis that the use of a more efficient control system results in a higher DF of the controlled variable. Starting from a basic structure of an ESO-type observer which, by its structure, allows the estimation of both the PMSM rotor speed and a term incorporating the disturbances on the system (from which, in this case, an estimate of the PMSM load torque can be extracted), four variants of observers are proposed, obtained by combining the use of a multiple neural network (NN) load torque observer and an RL-TD3 agent. The numerical simulations performed in MATLAB/Simulink validate the superior performance obtained by using properly trained RL-TD3 agents, both in the case of sensored and sensorless control.

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

AbstractThe fault detection with a single sensor cannot comprehensively use the multi‐sensor correlation information of underwater thruster. To solve the issue that in the case of entanglement, a kind of fault diagnosis method of underwater thruster based on the combination of current and rotational speed signal correlation analysis and support vector machine is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized, the variable sampling data points is adopted to refine the time of fault occurrence; Secondly, the cross correlation and autocorrelation coefficients of normalized current and speed signals are calculated based the variable sampling data points number and the correlation matrix is formed at different sampling time based on the cross correlation and autocorrelation coefficients. Finally, the support vector machine was applied to diagnose whether or not the fault produces and the time of fault occurrence with the correlation coefficients sequence. To verify the effectiveness of the proposed method, the fault simulation platform and data acquisition software are designed, the fault data in the case of thruster with entanglement are collected in time to test the proposed method. The results show that compared with the fault diagnosis method using only one speed or current signal, the proposed analytical method based on the correlation coefficients can fully extract the correlation information of underwater thruster fault, and the time extraction of fault occurrence is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

Designing a power system stabilizer using a hybrid algorithm by genetics and bacteria for the multi-machine power system

This research creates an optimal power grid stabilizer for four machines. Rotor speed signals are system inputs in the proposed design. To improve response, this system uses a conventional power system stabilizer (CPSS) and an optimal CPSS (OCPSS). The genetic optimization hybrid has a new structure, and the network generators use the bacterial foraging algorithm (BFA) with stabilizer system. The system set is tested by MATLAB software under various conditions to evaluate the designs. The experiment starts with a three-phase fault in line 3 that is fixed by breaking the line after 0.2 seconds. The simulation results show that after a short circuit in line 3, the proposed OCPSS design reaches damping after about a cycle of oscillation in 4 seconds. However, the conventional CPSS design achieves damping after four oscillations in six seconds. Simulations show that the proposed method is better than genetic algorithm (GA) and BFA. Power system oscillations are dampened faster and with lower amplitude when power system stabilizer (PSS) coordinate with the proposed optimization method. It also improves power system dynamics. We demonstrate with the proposed OCPSS stabilizer that advanced optimization systems can maximize system control capacity by utilizing conventional CPSS system advantages.

Bearing fault diagnosis based on speed signal and CNN model

In the fault diagnosis of the existing permanent magnet synchronous motor, the characteristics of the fault are often extracted based on the vibration acceleration signal. The acquisition of vibration acceleration requires the additional installation of expensive sensors, and the diagnosis effect is greatly affected by the surrounding environment. On the other hand, at this time, due to the influence of the load side, the fault characteristics of the bearing and other faults are easily submerged, resulting in a diagnosis failure or even a misdiagnosis. This paper proposes a bearing fault diagnosis method based on the motor speed signal. Combined with the CNN, the feature extraction and analysis of the rotational speed signal are carried out, and an improved algorithm is proposed by combining the artificially selected eigenvalues in the frequency domain. The experimental results show that this method can still complete the diagnosis of bearing faults well in the presence of misalignment fault interference, which shows the potential of deep learning technology represented by CNN in the use of rotational speed signals to diagnose various types of motor faults and provide experimental and theoretical basis for it.

The Hall Sensors Fault-Tolerant for PMSM Based on Switching Sensorless Control With PI Parameters Optimization

The failure of Hall sensors can give rise to loss of rotor position and speed information, which can result in loss of control of the speed loop, spatial decoupling errors, and affecting system stability in three-phase PMSM control system. To solve this problem, a Hall fault-tolerant control system based on sensorless state switching is proposed in this paper. Firstly, a three-phase PMSM control system based on FOC is established to analyze and clarify the importance of rotor position and speed signals. Secondly, a detection scheme based on Hall change edge signal is designed to detect the health status of Hall sensors and implement fault-tolerant state switching. For the stability of the system, a sliding mode observer is built to obtain the back electromotive force(back-EMF). In addition, the phase-locked loop is designed to extract the rotor position and speed information from the back-EMF. When the system enters a fault-tolerant state, changes in the speed feedback loop can affect the control performance of the system. Therefore, the speed loop PI parameter optimization scheme based PSO algorithm is proposed to optimize the performance of the fault tolerant control system. Finally, the effectiveness of the fault-tolerant method based on the sensorless state switching after particle swarm optimization is verified on a semi-physical platform (RCP-HIL). The experimental results show that the Hall fault-tolerant control system proposed in this paper can effectively detect Hall sensor faults and cut into the fault-tolerant control state, and can maintain the control system stable during fault-tolerant state switching. Furthermore, the control system can maintain an observation error of ±30 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rpm</i> under variable load or variable speed conditions at medium and high speeds when the system enters fault-tolerant control state.

Adaptive Cost Function Ridge Estimation for Rolling Bearing Fault Diagnosis Under Variable Speed Conditions

Rolling bearing health condition evaluation under time-varying operating speed requires information about shaft rotational speed. However, it is not always feasible to install a sensor such as a tachometer or an encoder to collect rotational speed signals due to space limitations or safety considerations. Time-frequency ridge estimation is an alternative approach that directly extracts the fault-related characteristic curves in a time–frequency representation (TFR) without phase reference. The cost function ridge estimation (CFRE) is the most widely used contemporary ridge estimation method. However, there is no explicit principle for the selection of search bandwidth. Moreover, the definitions of weight factor and cost function lack rationality, which causes the CFRE to degrade partially into coarse direct maximum ridge estimation (DMRE) or possibly extract the least frequency-varying curve. A time–frequency ridge estimation method, which is called adaptive CFRE (ACFRE), is developed in this study to address these deficiencies. We improve the original cost function and redefine its mathematical model to better balance searching for peak amplitude and guaranteeing a continuous curve. Numerical simulations and experimental investigations show that the proposed ACFRE is more robust to interferences derived from adjacent curves and noise than CFRE and another well-known ridge estimation approach, fast path optimization (FPO). The proposed method has a smaller root mean square error (RMSE) in estimating bearing characteristics under variable speed conditions.

Evaluation of exponential moving average application to smooth the power output of wind turbine with different control modes

This paper focused on evaluating the application of exponential moving average method into wind turbine to smooth its power output without an energy storage system or an anemometer. Wind turbine control modes including active power control mode and rotor speed control mode are considered. For each control mode, two positions of the Exponential Moving Average method in controller were compared to choose the best position. Additionally, the impact of smoothing factor on wind turbine performance was also considered to determine a reasonable value of the smoothing factor for each control mode. Simulation results in MATLAB/Simulink indicated that, for wind turbine using rotor speed control mode, the Exponential Moving Average method should be applied to reduce the variation of actual rotor speed signal while for wind turbine with the power control mode, it should be used to smooth reference power signal. From the performance of wind turbine with different smoothing factor values, we can suggest that the smoothing factor value should be set at 0.5 and 0.4 for the power control mode and the rotor speed control mode, respectively.

Control law design for the air-turbine-generator set of a fully submerged 1.5 MW mWave prototype. Part 1: Numerical modelling

A fully-submerged bottom-standing 1.5 MW wave energy converter (WEC) equipped with a membrane-pump system is under development for installation at a test site off the coast of Pembrokeshire, Wales, UK. The system comprises several flexible-membrane air cells that are compressed and sucked by the action of the waves, rectifying valves, low-pressure and high-pressure ducts, and a unidirectional air turbine that drives an electrical generator. This two-part paper reports the design and testing of an effective control law to be implemented into the programmable logic controller of the membrane-pump turbine-generator set, allowing efficient and safe operation for the wave climate at the Pembrokeshire test site. Part 1 of this paper concerns developing the design strategy of the control laws, the numerical model, and the performance evaluation of the WEC power take-off system. A genetic algorithm is used to design control laws to maximise each sea state's WEC time-average efficiency. An adaptive control algorithm is devised by cross-correlating the control laws parameters and the rotational speed. The final result is a control law that uses input the rotational speed signal and airflow density. It adapts to the sea state conditions while maximising the time-average total efficiency of the membrane-pump WEC for deployment off the Pembrokeshire coast.

Use of IMU in Differential Analysis of the Reverse Punch Temporal Structure in Relation to the Achieved Maximal Hand Velocity.

To achieve good performance, athletes need to synchronize a series of movements in an optimal manner. One of the indicators used to monitor this is the order of occurrence of relevant events in the movement timeline. However, monitoring of this characteristic of rapid movement is practically limited to the laboratory settings, in which motion tracking systems can be used to acquire relevant data. Our motivation is to implement a simple-to-use and robust IMU-based solution suitable for everyday praxis. In this way, repetitive execution of technique can be constantly monitored. This provides augmented feedback to coaches and athletes and is relevant in the context of prevention of stabilization of errors, as well as monitoring for the effects of fatigue. In this research, acceleration and rotational speed signal acquired from a pair of IMUs (Inertial Measurement Unit) is used for detection of the time of occurrence of events. The research included 165 individual strikes performed by 14 elite and national-level karate competitors. All strikes were classified as slow, average, or fast based on the achieved maximal velocity of the hand. A Kruskal–Wallis test revealed significant general differences in the order of occurrence of hand acceleration start, maximal hand velocity, maximal body velocity, maximal hand acceleration, maximal body acceleration, and vertical movement onset between the groups. Partial differences were determined using a Mann–Whitney test. This paper determines the differences in the temporal structure of the reverse punch in relation to the achieved maximal velocity of the hand as a performance indicator. Detecting the time of occurrence of events using IMUs is a new method for measuring motion synchronization that provides a new insight into the coordination of articulated human movements. Such application of IMU can provide additional information about the studied structure of rapid discrete movements in various sporting activities that are otherwise imperceptible to human senses.

Synchronous Vibration Suppression of Magnetically Suspended Rotor System Using Improved Adaptive Frequency Estimation

The rotor system of magnetically suspended control moment gyro (MSCMG) inevitably has unbalanced amount due to errors in its materials and processing accuracy. The mass unbalance of the rotor will cause the system to generate synchronous vibration and then transmit it to the spacecraft platform through the base, which will seriously affect the imaging quality. The realization of synchronous vibration suppression depends seriously on the rotor speed signal. When the Hall speed sensor has a large measurement error or fails, it is necessary to adaptively estimate the rotor speed to suppress the unbalance vibration of the rotor system. This paper proposes an improved adaptive frequency estimation (IAFE) method. This algorithm uses the characteristics of the rotor radial displacement sensor signal to adaptively estimate the rotor speed, and then suppress the synchronous current generated by the control system. It maintains the stability of the system by changing the phase shift angle at different frequencies, and the residual displacement stiffness force is also compensated. The simulation results show that the proposed method is easy to be implemented, and it can achieve high-precision suppression of unbalanced vibration.

Torque measurements from MW wind turbine Gearboxes: a system identification approach

The gearbox is a critical component of modern MW wind turbines. An accurate model of the gearbox dynamics is needed to improve gearbox design, develop advanced control algorithms, and more effective fault diagnosis tools which could lead to lower the cost of energy from wind. The objective of this paper is to investigate how torque measurements can be used in a data-driven framework to build dynamic models of wind turbine gearboxes.An initial torsional model has been derived from first principles considering the stiffness of the gears, shafts, and structural components in the gearbox together with the mechanical components of the test bench. This model has been used to create simulated data of the experiments performed on gearboxes and to apply system identification techniques to the simulated signals, with a focus on predictor based subspace identification methods. System identification has been applied to torque and speed data measured on physical tests of two 3.4MW gearboxes. Gearbox excitation frequencies and their harmonics dominate the measured signals and disturb the system identification algorithms. Several techniques have been investigated to remove the shaft rotation and gear mesh frequency harmonics of the torque and rotational speed signals based on time synchronous averaging.

A new robust sensorless vector-control strategy for wound-rotor induction motors

ABSTRACT Aim of this study is to investigate a sensor-less speed-observer with the principles of vector control for the industrial applications of Wound-Rotor Induction Motors (WRIM). The proposed estimation method is based on a simple topology to efficiently predict the rotor-speed signal with the aid of the phase axes relations of the adopted motor and using the flux orientation scheme with the indirect type (IRFOC). To effectively verify the efficacy of the presented sensor-less control system, overall results are carried out. The realised analysis validates the proposed estimation method for the rotor-speed of WRIM, which ensures its capability for sensor-less vector control methodology of the adopted system.

Attention Recurrent Neural Network-Based Severity Estimation Method for Interturn Short-Circuit Fault in Permanent Magnet Synchronous Machines

With the development of smart factories, deep learning, which automatically extracts features and diagnoses faults, has become an important approach for fault diagnosis. In this article, a novel interturn short-circuit fault (ISCF) diagnosis approach using an attention-based recurrent neural network is proposed. An encoder-decoder architecture using an attention mechanism diagnoses the ISCF by estimating a fault indicator that directly reflects the severity of the fault, using currents and rotational speed signals as inputs. The attention mechanism helps the decoding process in accurate diagnosis and solves the long-term dependence problem of the encoder-decoder structure. The proposed algorithm uses only three-phase current and rotational speed as the inputs to evaluate the severity of the ISCF and enable early stage diagnosis of ISCF. The diagnosis of ISCF is achieved in various operating points and fault conditions, and no additional sensors, such as voltage and vibration sensors, are required. Experimental results for various operating and fault conditions demonstrate that the proposed method effectively diagnoses ISCFs.

Output Delay Sliding Mode Tracking Control of SRM based on Multi-innovation Model Identification

The drive system of switched reluctance motor (SRM) is a complex nonlinear system that is composed of many links. The delay in the measurement of the speed and position signal of SRM is caused by the factors that affect the measuring sensor. To effectively improve the influence of the SRM rotor position and speed signal delay on the system performance, a sliding mode position tracking method based on output delay observation was proposed in this study. First, the model was discretized according to the structure and characteristics of SRM and the mathematical parameters of the system were identified using a multi-innovation stochastic gradient (MISG) algorithm. Second, a delay state observer was constructed on the basis of an SRM system model with output delay. Then, the sliding mode tracking control method based on the delay state observation compensation was proposed and combined with sliding mode control theory. Lastly, the effectiveness of the designed model parameter identification, delay state observation, and output delay control methods were compared through numerical simulation. Results show that when uncertain factors, such as noise, are present in the system, the MISG identification method can rapidly and accurately identify the parameters of the SRM model compared with the stochastic gradient identification method; the identification accuracy of the former is four times higher than that of the latter. Similarly, the sliding mode position tracking control method based on output delay observer can rapidly and accurately track the position and speed within 0.5 s. However, its position (0.2 rad) and velocity (0.233 rad/s) tracking exhibit large steady-state errors when no delay observation compensation is present. The proposed method not only demonstrates high position tracking accuracy, but also possesses strong robustness to output delay.

타이어 저압감지를 위한 타이어 공진 주파수 추정

Indirect tire pressure monitoring system(i-TPMS) monitors the inflation pressures in pneumatic tires by using wheel speed signals acquired from the anti-lock braking system(ABS). Frequency analysis and wheel radius analysis are performed at the same time in order to monitor the tire pressures indirectly. The resonance frequency of the tire torsional vibration decreases as the tire pressure decreases. It is necessary to estimate the resonance frequency in order to detect a reduction in tire pressure. This paper discusses the method of estimating the resonance frequency with the rotational wheel speed signal. First, the toothed wheel error of the wheel speed sensor and the wheel speed corrected with the toothed wheel error are calculated from the wheel speed signals. The engine noise caused by the explosion in the four-stroke of engine is eliminated by the notch filter, which is designed by using the engine rotational speed. Finally, the resonance frequency is evaluated via model parameter estimation based on the autoregressive(AR) model. Vehicle tests have been conducted in order to evaluate the proposed estimation method of resonance frequency. The findings of the vehicle test have shown that the proposed method provides satisfactory performance for the warning indication of tire pressure loss.