Angular Signal Research Articles

Effect of rotation speed fluctuation on rotor noise generation: A numerical and experimental study

The practical operations of rotors for unmanned aircraft systems can undergo time-varying rotation speeds due to various uncertainty factors. This work investigates the aeroacoustic effect of the rotor’s rotation speed fluctuation using both numerical and experimental approaches. The measured real-time angular speed signal reveals that a hovering rotor experiences highly deterministic rotation speed variations in one revolution, which are closely related to the motor property. Delayed detached eddy simulations are performed, and the fluctuating rotation speed is realized by controlling the mesh displacement at each time step. The results suggest that significant additional tones are produced at the excitation frequency and neighbour frequencies, even though the amplitudes of the rotation speed fluctuations at the dominant frequencies are much smaller than the mean rotation speed. By comparing the full signal to individual harmonic components, it is found that the phase difference between each mode is unimportant. Therefore, the total aeroacoustic effect can be regarded as a linear superposition of different harmonic components. Aeroacoustic source analysis is also performed to isolate the contribution of various source types and correlate the far-field noise with the near-field source distribution. The results show that the extra tonal noise is mostly dominated by the loading noise, with a weaker contribution from the thickness noise. The agreement between the experiments and the simulations indicates that the rotation speed fluctuation is likely responsible for the generation of middle-frequency tonal noise at harmonics of the blade passing frequency.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

LabVIEW-based rotary balance data synchronization acquisition system design

Abstract To solve the problem that many high-precision rotating balance bridge output signals and angular phase signals need to be measured synchronously in wind tunnel force measurement experiments, a rotating balance data synchronization acquisition system is designed based on LabVIEW and NI PXIe acquisition board. LabVIEW graphical programming is applied, combined with STM32F407VET6 processor embedded development of the timing divider, using the equal interval method to obtain the required synchronization trigger signals and use them as the digital trigger signals of the NI PXIe acquisition card. In addition, multi-threaded programming technology is used to develop the upper computer software to realize the storage, display, and analysis of the collected data. The test proved that the system has high testing accuracy, a short development cycle, embedded visualization development is easy to debug, stable operation, and real-time and reliable data synchronization acquisition.

A fault diagnosis method for variable speed planetary gearbox based on ADGADF and Swin Transformer

The vibration signal of planetary gearboxes under variable speed conditions shows non-stationary characteristics, indicating that fault diagnosis has become more complex and challenging. In order to more accurately diagnose faults in planetary gearboxes under variable speed conditions, a new method is proposed based on the angular domain Gramian angular difference field (ADGADF) and Swin Transformer. This method initially employs the chirplet path pursuit (CPP) algorithm to fit the speed curve of the original time-domain signal and then combines the speed curve with computed order tracking (COT) to achieve equal angle resampling of the time-domain signal, obtaining a stationary signal in the angular domain. On the basis of the above, the angular domain signal is creatively encoded into the two-dimensional images using the Gramian angular field (GAF), which accurately represents the fault characteristics of the original signal. Finally, the Swin Transformer network, with efficient global feature extraction capability, is used to learn advanced features from the images, achieving accurate fault recognition and classification. The proposed method is verified by experiment on the planetary gearbox and its performance is compared with several common coding methods and intelligent diagnosis algorithms. The experimental results show that the proposed method reaches an accuracy of up to 99.8%. In addition, its performance in accuracy, precision, recall, F1-score and the confusion matrix is superior to traditional diagnostic methods. It also offers the advantage of strong robustness.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

A Novel In-Line Measurement and Analysis Method of Bubble Growth-Dependent Strain and Deformation Rates during Foaming.

Bubble growth processes are highly influenced by the elongational viscosity of the blowing agent-loaded polymer melt. Therefore, the elongational viscosity is an important parameter for the development of new polymers for foaming applications, as well as for the prediction of bubble growth processes. Thus, knowledge of the initial expansion and deformation behavior in dependency on the polymer, the blowing agent concentration, and the process conditions is necessary. This study presents a novel method for the in-line observation and analysis of the initial expansion and deformation behavior within the bead foam extrusion process. For this purpose, nitrogen as the blowing agent was injected into the polymer melt (PS and PLA) during the extrusion process. The in-line observation system consists of a borescope equipped with a camera, which was integrated into the water box of an underwater pelletizer. The camera is controlled by a developed trigger by means of angular step signal analysis of a rotary encoder on the cutter shaft of the underwater pelletizer. Thus, images can be taken at any time during the foaming process depending on the cutter position to the die outlet. It is shown that the developed method provides reliable results and that the differences of the initial expansion and deformation behavior during bubble growth can be analyzed in-line in dependency on real foaming process conditions and the type of polymer used.

Motor Bearing Fault Diagnosis in an Industrial Robot Under Complex Variable Speed Conditions

Abstract Motor bearing is the key vulnerable part of the servomotor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong nonstationary characteristics. Early nonstationary fault signals are often weak and submerged in background noise. The nonstationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the nonstationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which are representative of the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.

Determination of the dynamics of the wheel of a mobile machine

A sensor, which consists of a three-axis accelerometer, a gyroscope, and a magnetometer, has been developed to determine the dynamics of the wheel of a mobile machine. The sensor is connected to a microcontroller, which transmits the received data via a 2.4 GHz channel. It is mounted coaxial to the center of the wheel. At the first processing stage, the accelerometer, gyroscope, and magnetometer data values are adjusted. The corrected accelerations and angular velocities signal are processed using a Butterworth filter. Filter Madgwick determines the angles of orientation of the sensor in space. In the next step, we remove the centrifugal component from the accelerations. Next, the gravitational component is subtracted from the accelerations, and its real value is obtained. The speed of the wheel is obtained by integrating accelerations. The Kalman filter processes the angular velocities, acceleration, and velocities. The measurement noise covariance matrix and the evaluation process matrix are calculated based on sensor errors. The results of experimental studies of the proposed method and sensor for determining the wheel dynamics of mobile machines on tractors with a 4x4 wheel arrangement were carried out. The sensors were installed on all wheels of the tractor. The developed wheel dynamics sensor allows us to determine the slippage of the engines of a mobile machine. This method of determining slipping is entirely accurate and does not require intervention in the design of the machine. The determination of the speed of rotation of the wheels of a mobile machine is based on the determination of the angular speed of rotation, which is measured with a gyroscope and accelerometer located in the center of the wheel of a mobile machine. Keywords: sensor, dynamics, wheel, mobile car, filter.

A Tacholess Order Tracking Method Based on the STFTSC Algorithm for Rotor Unbalance Fault Diagnosis Under Variable-Speed Conditions

Abstract Due to the fact that rotors usually operate in a non-stationary mode with changing speeds, the conventional rotor unbalance detection method based on the stationary signal will produce a major “spectrum ambiguity issue” and affect the accuracy of rotor unbalance detection. To this end, a tacholess order tracking method based on the STFTSC algorithm is suggested in this study, where the STFTSC algorithm is developed by combining the short-time Fourier transform and the seam carving algorithm. First, the STFTSC algorithm is utilized to accurately extract the instantaneous frequency (IF) of the rotor and calculate the instantaneous phase under variable-speed conditions. Subsequently, the original signal is resampled in the angular domain to transform the non-stationary time domain signal into a stable angle domain signal, eliminating the effect of the speed variations. Finally, the angular domain signal is transformed into the order domain signal, which uses the discrete Fourier transform and the discrete spectrum correction method to identify the amplitude and phase corresponding to the fundamental frequency component of the signal. The simulation results show that the IF extracted by the STFTSC algorithm has higher extraction accuracy compared with the traditional STFT spectral peak detection method and effectively eliminates the effect of speed fluctuations. A rotor dynamic-balancing experiment shows that the unbalance correction effect based on the STFTSC algorithm is remarkable, with the average unbalance amount decrease rate on the left and right sides being 90.02% and 92.56%, respectively, after a single correction.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

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.

MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection.

The time-varying and individual variability of surface electromyographic signals (sEMG) can lead to poorer motor intention detection results from different subjects and longer temporal intervals between training and testing datasets. The consistency of using muscle synergy between the same tasks may be beneficial to improve the detection accuracy over long time ranges. However, the conventional muscle synergy extraction methods, such as non-negative matrix factorization (NMF) and principal component analysis (PCA) have some limitations in the field of motor intention detection, especially in the continuous estimation of upper limb joint angles. In this study, we proposed a reliable multivariate curve-resolved-alternating least squares (MCR-ALS) muscle synergy extraction method combined with long-short term memory neural network (LSTM) to estimate continuous elbow joint motion by using the sEMG datasets from different subjects and different days. The pre-processed sEMG signals were then decomposed into muscle synergies by MCR-ALS, NMF and PCA methods, and the decomposed muscle activation matrices were used as sEMG features. The sEMG features and elbow joint angular signals were input to LSTM to establish a neural network model. Finally, the established neural network models were tested by using sEMG dataset from different subjects and different days, and the detection accuracy was measured by correlation coefficient. The detection accuracy of elbow joint angle was more than 85% by using the proposed method. This result was significantly higher than the detection accuracies obtained by using NMF and PCA methods. The results showed that the proposed method can improve the accuracy of motor intention detection results from different subjects and different acquisition timepoints. This study successfully improves the robustness of sEMG signals in neural network applications using an innovative muscle synergy extraction method. It contributes to the application of human physiological signals in human-machine interaction.

Enhanced Misalignment Estimation of Orbital Angular Momentum Signal Based on Deep Recurrent Neural Networks

This article presents a deep-learning-based approach to quantitatively extract misalignment information in the orbital angular momentum (OAM) radio communication systems. It is shown that the misaligned field exhibits unique sequence-based features for different misalignment information. Consequently, recurrent neural network, as a powerful method to deal with sequential data, is introduced to extract its intrinsic features and estimate the misalignment values. To further reduce the total number of parameters as well as improve the performance, a gated recurrent unit is employed to construct the recurrent layer. As far as we know, this is the first use of deep learning for the estimation of the OAM misalignment information in radio frequency. The setup of the training data and the performance of the proposed model under four different misalignment cases are demonstrated. To further validate the reliability of the proposed models, the trained model is evaluated on the sample carrying 20% Gaussian noise. Further, the effects of the position deviation and the number of samples are also investigated. Besides, the comparison with other methods are presented. It is illustrated that the tailored model can reach the same measurement resolution as the previous literatures while requiring only one-eighth of the sampling points.

Analysis of air bearing torsion pendulum moment of inertia measurements including nonlinear oscillation and damping.

For precise measurement of the moment of inertia (MOI) of asymmetric objects, using an air bearing torsional pendulum, the nonlinearity of the torsion bar and the damping effect caused by the interaction between air and sample need to be included in the analysis. The instantaneous undamped natural frequency and instantaneous damping coefficient of the motion are obtained from the Hilbert transform of the angular displacement signal and are used for data fitting to calculate the MOI. Measurements for algorithm verification were made using a special sample with large windvanes to increase the damping effect. The nonlinear analysis of the MOI experimental results indicated a relative error of less than 0.1% compared with 0.3% for classical linear analysis of the data.

Characteristics of a Gaussian focus embedded within spiral patterns in common-path interferometry with phase apertures

A phase-only method is proposed to transform an optical vortex field into desired spiral diffraction–interference patterns. Double-ring phase apertures are designed to produce a concentric high-order vortex beam and a zeroth-order vortex beam, and the diffracted intensity ratio of two beams is adjustable between 0 and 1. The coherent superposition of the two diffracted beams generates a brighter Airy spot (or Poisson spot) in the middle of the spiral pattern, where the singularity for typical vortex beam is located. Experiments employing circular, triangular, and rectangular phase apertures with topological charges from 3 to 16 demonstrate a stable, compact, and flexible apparatus for vortex beam conversion. By adjusting the parameters of the phase aperture, the proposed method can realize the optical Gaussian tweezer function and the optical vortex tweezer function simultaneously along the same axis or switch the experimental setup between the two functions. It also has potential applications in light communication through turbulent air by transmitting an orbital angular momentum-coded signal with a concentric beacon laser.

Frame Angular Velocity Control Design of SGCMG for Unmanned Two-Wheeled Motorcycle

In contrast to driverless cars and other three-wheeled and four-wheeled motorcycle vehicles, driverless two-wheeled motorcycles have the problem of maintaining balance. In this paper, we propose the design of an SGCMG frame angular velocity controller to realize the balance control of the motorcycle under static and dynamic working conditions. Meanwhile, since the roll angular acceleration of the actual body movement of the cross roll cannot be obtained directly, this paper proposes a Kalman filtering method based on the nonlinear dynamics model of the motorcycle to obtain a reliable angular acceleration signal. First, we modeled the dynamics of the motorcycle by analyzing the various types of moments generated by the motorcycle equipped with the SGCMG under static and dynamic conditions; Then, the design of the angular velocity control of the SGCMG frame was carried out with the feedback and through MATLAB/Simulink simulation to restore various types of actual working conditions to verify the controller has good robustness; Finally, we have completed the test of the controller using the above filtering method on the real vehicle with an embedded system and compared the effect with other controllers, obtained the results that the body is stable and balanced under static conditions and the applied load can automatically find a new balance point, so as to prove the effectiveness of the designed control.

Generalized broadband mode decomposition method and its application in fault diagnosis of variable speed spherical roller bearing

Fault signals of roller bearing with variable speed often present non-stationary characteristics which are difficult to be accurately extracted by existing analytical methods. The previously proposed broadband mode decomposition (BMD) method is to search in an associative dictionary library containing all signals to extract stationary signals. However, when applied to the variable speed signals interfered by strong noise, BMD will have a large deviation. Therefore, considering the shortcomings of BMD, a generalized broadband mode decomposition (GBMD) method is proposed by combining generalized demodulation and BMD. Firstly, the vibration signal is decomposed by GBMD. Secondly, the first three components are resampled in the angular domain. Finally, the envelope order spectrum of the angular signal is analyzed. The analysis shows that, compared with EEMD and BMD, GBMD not only has strong decomposition capability, but also can effectively extract fault features to achieve fault diagnosis of spherical roller bearing with variable speed.

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

This paper presents some advances in condition monitoring for rotary machines (particularly for a lathe headstock gearbox) running idle with a constant speed, based on the behaviour of a driving three-phase AC asynchronous induction motor used as a sensor of the mechanical power via the absorbed electrical power. The majority of the variable phenomena involved in this condition monitoring are periodical (machines having rotary parts) and should be mechanically supplied through a variable electrical power absorbed by a motor with periodical components (having frequencies equal to the rotational frequency of the machine parts). The paper proposes some signal processing and analysis methods for the variable part of the absorbed electrical power (or its constituents: active and instantaneous power, instantaneous current, power factor, etc.) in order to achieve a description of these periodical constituents, each one often described as a sum of sinusoidal components with a fundamental and some harmonics. In testing these methods, the paper confirms the hypothesis that the evolution of the electrical power (instantaneous and active) has a predominantly deterministic character. Two main signal analysis methods were used, with good, comparable results: the fast Fourier transform of short and long signal sequences (for the frequency domain) and the curve fitting estimation (in the time domain). The determination of the amplitude, frequency and phase at origin of time for each of these components helps to describe the condition (normal or abnormal) of the machine parts. Several achievements confirm the viability of this study: a characterization of a flat driving belt condition and a beating power phenomenon generated by two rotary shafts inside the gearbox. For comparison purposes, the same signal analysis methods were applied to describe the evolution of the vibration signal and the instantaneous angular speed signal at the gearbox output spindle. Many similarities in behaviour among certain mechanical parts (including their electrical power, vibration and instantaneous angular speed) were highlighted.

Quadratic Displacement Operators—Theory and Application to Millimeter-Wave Channel Tracking

First, a family of quadratic displacement operators based on group Fourier Transform has been proposed for joint distribution analysis. Second, considering the quadratic displacement operators, a novel millimeter-wave massive MIMO channel tracking has been proposed in Time-Angle (TA) plane. Due to a poor scattering environment, millimeter-wave communication suffers from an ill-conditioned channel matrix. Computationally effective compressed sensing has been proposed to estimate a few dominant paths. These methods rely on orthogonal pilot signals to estimate the channel reliably. However, applying compressed-sensing solutions to the continuous channel leads to significant pilot overhead. To reduce the pilot overhead, one needs to consider the channel dynamics, particularly the spectral overlap between the channel samples. Considering that channel spectral properties can be represented as a joint time-angle distribution, we propose a novel quadratic displacement operator in TA-plane. Considering group Fourier transform in TA-plane, we show that the subderivative of an angular parameter is the dual group of the original angular signal. For a group transform defined over the finite Abelian group, with respect to the finite-dimensional antenna arrays, the purposed time-angle representation matches the cover space of manifold defined in the virtual channel model recommended by A. Sayeed. In TA-plane, the quadratic displacement operator maps old time-angle coordinate onto a new coordinate by taking to account the local spectral properties of the channel samples. By applying the purposed quadratic displacement operator to the Saleh-Valenzuela channel model, the time evolution operator for directional millimeter-wave channel has been derived. Assuming independently identically distributed multipath contribution, we show that channel Wigner distribution is the direct sum of the cluster-wise Wigner distribution. Accordingly, we have shown that the continuous linear time-variant channel transfer function can be represented as the Hadamard product between Wigner distribution of the channel and Saleh-Valenzuela model. Considering the fact that the density matrix is a Weyl correspondence of Wigner distribution, a union of subspaces method has been employed to construct the density matrix for channels samples with slightly different states. Numerical results have been presented to evaluate the performance of the proposed channel tracking method.

Экспериментальная проверка способа выравнивания мощностей цилиндров многоцилиндрового дизеля

Purpose: The purpose of the present work is to test experimentally equalization method for the outputs of multi-cylinder diesel engine cylinders basing on energy minimization of amplitude spectrum low-frequency band for diesel crankshaft instantaneous angular speed signal. Methods: The work uses the methods of digital signal processing, spectral analysis, differential calculus, multidimensional undifferentiated function optimization, experimental results processing. Results: The fulfilled experimental studies at bench diesel generator device as a part of locomotive diesel D50 (6CHN31.8/33) and traction generator MPT 84/39 ensure the possibility to use proposed earlier method for power equalization for the cylinders of multi-cylinder diesel engine basing on energy control of amplitude spectrum low-frequency band of crankshaft instantaneous angular speed signal. Unrvenness index for cylinder power is proposed, its sensitivity to cyclic supply change and the advance angle of fuel supply to diesel engine individual cylinders is estimated. Practical significance: The possibility to apply being proposed equalization method for the powers of multi-cylinder diesel engine cylinders for to adjust fuel equipment after diesel engine repair as with the systems of electronic fuel supply control as well as with hydromechanical fuel equipment has been confirmed experimentally. The use of given method eliminates the need to measure the temperature of exhaust gases and flash pressure in cylinders for fuel equipment adjustment that’s especially important for high-speed diesel engines which cylinder cover construction doesn’t imply indicated valves.