A novel method of intelligent diagnosis is proposed for structural faults in low-speed rotating machinery. It uses a hybrid scheme to automatically identify health states in a complex mechanical ...system by combining an improved mode decomposition approach, a Gramian angular summation field, and a convolutional neural network. The proposed method is tested with a data set affected by noise to evaluate its performance and generalizability that are both essential in fault diagnosis. Experimental results show that the proposed scheme is superior to traditional machine learning methods. It not only provides a more efficient and widely applicable way to learn intrinsic fault signatures, but it also has enhanced feature learning capability to improve precision and generalizability. Finally, the reasons for the method's high performance are analyzed to determine the best general features for an adaptive classifier that is generalizable to diverse operating conditions.
Constant frequency torque controller-based direct torque control (CFTC-DTC) is emerging as a powerful control strategy for a high-performance control of alternating-current motor drives. Compared to ...DTC, the CFTC-DTC has less torque and flux ripples with a fixed switching frequency. Nevertheless, it has a limitation of the torque-loop bandwidth, as it uses the sampling frequency from a digital signal processor to generate a triangular-based carrier. The switching frequency of the control system is determined by the carrier frequency of the CFTC. The frequency limitation of CFTC leads to sector-flux droop, which causes a distortion in the motor phase current and aggravation in the torque and speed waveforms at low speed regions. In this article, we propose a modification of the frequency carriers of the CFTC-DTC of induction motors to increase the switching frequency and torque loop bandwidth by replacing the triangular-carrier-based waveform with a ramp-carrier-based waveform. The proposed method is verified by simulation and experimental results showing its excellent performances in steady- and transient-state operations.
•A new method is proposed by combination Teager energy operator and CEEMD.•Teager energy operator is used to enhance the signal impact components.•CEEMD algorithm is carried out to extract bearing ...fault through IMF decomposition.•Experimental results validate the effectiveness of the proposed method.
The fault signals of low-speed rolling elements bearing are non-stationary and non-linear, and consequently it is difficult to extract the fault characteristics by the traditional time and frequency domains analysis methods. Furthermore, the vibration signals suffer from severe signal attenuation and noise corruption during the signal transmission process. In order to effectively enhance and extract the fault characteristics from weak bearing signal, it requires effective signal processing strategies or high sensitive sensors to detect the low energy bearing vibration signals. In this paper, one such signal processing method is proposed to detect fault characteristics combined Teager energy operator and Complementary Ensemble Empirical Mode Decomposition (CEEMD). In this method, firstly Teager energy operator is used to strengthen the signal after wavelet noise reduction since it has good temporal resolution and adaptive ability for signal transient changes, and has unique advantages in detecting signal impact characteristics. Then CEEMD algorithm is carried out to extract bearing fault through Intrinsic Mode Function (IMF) decomposition. The proposed method is validated by a scaling model test rig of a wind turbine. The results validate that the method can effectively extract the fault characteristics of low-speed bearings and identify the bearing fault.
Machinery fault diagnosis is an attractive but challenging task, especially for low-speed conditions. Therefore, a new discriminative approach that introduces robust principal component analysis ...(RPCA) and multikernel to deep neural networks is proposed to perform intelligent fault diagnosis. First, RPCA is applied to extract fault signals from extreme background noise based on its sensitivity to grossly corrupted data. Second, two cascaded multikernel principal component analysis stages with additional robustness to distortions in feature extraction are used to enhance the energy of spectrum symptom and overcome the tricky issues of low-speed machinery. Especially, the multikernel is introduced into the basic PCA filters to learn the data-adapting convolution filter and gain additional robustness to nonlinearity in the signal. Finally, the proposed method is demonstrated on signals from laboratory tests (with a slightly damaged defect in a bearing) and structural fault data, outperforming those of traditional machine learning and classical deep learning methods. Moreover, hidden information of the network is visualized to analyze the reasons for its high performance.
The article proposes a low-speed model predictive control (LSMPC) based on modified extended state observer with low-resolution encoder for the field modulated stator permanent magnet arc motor. The ...resolution of the encoder and the large torque ripples caused by the motor structure limit the expansion of the drive system to lower speeds. In order to reduce the contradiction between high bandwidth and high noise, and improve the performance of the drive system in low speed, a modified linear extended state observer with a feedforward compensation is designed to estimate the speed and disturbance. Moreover, a dead time compensation is equipped to reduce the voltage distortion in light load and low speed. The experimental results verify the effectiveness of the proposed method in low speed with low-resolution encoder.
High-speed laser cladding technology can significantly improve the efficiency of coating preparation and effectively widen the application range of laser cladding. In this study, the Ni45 powders ...were deposited on steel substrate by traditional low speed laser cladding and high-speed laser cladding process, respectively. The cladding efficiency, surface forming, cross-sectional microstructure, microhardness, wear and corrosion resistance properties of the traditional and high-speed laser cladded Ni45 alloy coatings were compared. It can be seen that the thickness of the high-speed laser cladding coating was much thinner than that of the traditional laser cladding coating. Compared with traditional laser cladding, high-speed laser cladding could achieve a cladding speed of 76.86 m/min and a cladding efficiency of 156.79 cm2/min. The microstructure of the two kinds of coatings shows the same growth law, but the microstructure in high-speed laser cladding was smaller and denser, and the columnar crystal interval was narrower, only about 6 μm. It is found that the cooling rate of the traditional laser cladding coating was smaller than that of the high-speed laser cladding, and as the cladding speed increased, the cooling rate became higher and higher. The cross-section microhardness of the traditional laser cladding coating was relatively uniform of 337 HV0.2, while the microhardness of high-speed laser cladding surface increased to about 543 HV0.2. In addition, the wear and corrosion resistance of high-speed laser cladded coatings were better than that of traditional laser cladded coatings. As the cladding speed increased, the wear and corrosion resistance of the cladded coatings became better.
•High-speed laser cladding coating had low roughness and thin coating.•The cooling rate increased exponentially with a decrease of secondary dendrite arm.•The wear and corrosion resistance increased with the increase of cladding speed.•The microstructure was refined continuously with the increase of cladding speed.
This article proposes a novel digital predistortion (DPD) scheme to linearize frequency multiplier (FX)-based vector signal sources that are subject to constrained transmitter and observation ...receiver bandwidth. Specifically, the proposed technique aims to reduce the digital-to-analog converter (DAC) sampling speed and the bandwidth of the transmitter RF front-end required by conventional DPD schemes for FX-based signal generation while maintaining excellent linearized output signal quality. This article starts by formulating an additive error model for an FX driven by a <inline-formula> <tex-math notation="LaTeX">D</tex-math> </inline-formula>th root function subject to bandwidth constraint. This model is then utilized to derive the expression of the proposed DPD model and underlying DPD training algorithm while allowing for the reduction of the required transmitter observation receiver (TOR) bandwidth. The extent of the TOR bandwidth relaxation is determined based on the bandwidth of the <inline-formula> <tex-math notation="LaTeX">D</tex-math> </inline-formula>th root function output. Using the proposed DPD scheme, extensive simulations and measurements carried out on two different FXs revealed a 22-25-dB improvement in adjacent channel power ratio (ACPR), when the transmitter and receiver bandwidths are limited to 4<inline-formula> <tex-math notation="LaTeX">\times</tex-math> </inline-formula> and 3<inline-formula> <tex-math notation="LaTeX">\times</tex-math> </inline-formula> the signal bandwidth, respectively. Moreover, the error vector magnitude (EVM) after DPD is reduced from 13.6%-21.1% to 1%-1.6% when the transmitter and receiver bandwidths are reduced down to 2<inline-formula> <tex-math notation="LaTeX">\times</tex-math> </inline-formula> and 1<inline-formula> <tex-math notation="LaTeX">\times</tex-math> </inline-formula> the signal bandwidth, respectively.
•Gasoline contaminants contribute to enhance pre-ignition.•Contaminant effect on pre-ignition is directly related to gasoline formulation.•Detergent additive can have both a positive and a negative ...effect on pre-ignition.•The correlation emissions and pre-ignition is valid for the selected contaminants.•Unwashed gums can detect and quantify diesel and detergent within gasoline fuel.•The results suggest a correlation between unwashed gums and low speed pre-ignition frequency.
Fuel composition variability affects many aspects of engine operation. This includes the propensity to trigger abnormal combustions. Fuel logistic can lead to incorporating different contaminants into gasoline. Diesel fuel or over-concentrated detergent additives are two examples. They require a qualification and quantification within the gasoline fuel and an evaluation of their impact on engine performances.
We consider both contaminants in this work and it addresses simultaneously their identification within a gasoline fuel and their impact on abnormal combustion. The study evaluates contaminants propensity to trigger Low Speed Pre-Ignition (LSPI). The latter is a stochastic abnormal combustion leading to engine damage related to fuel quality. A novel method based on unwashed gums is used to characterize qualitatively and quantitatively these contaminants. Engine tests led to the conclusion that diesel contamination contributes to increase LSPI frequency and severity. However, this increase depends on the base fuel heavy aromatic content. Deposit control additive (DCA) increases slightly LSPI frequency compared to diesel contaminant. The study also suggests that such additive can contribute to recover a less sensitive combustion system once it has been running with a diesel contaminated gasoline. Finally, this work explores the possibility of correlating fuel propensity to trigger LSPI through unwashed gums (UWG) measurement. It concludes that the approach seems sensible but requires further work to be confirmed.
Utilizing NICER observations, we present an analysis of the soft X-ray rebrightening event of GRS 1915+105 observed in 2021. During this event, we observed the emergence of a stable long-lasting ...low-frequency quasi-periodic oscillation (LFQPO) with frequencies ranging from 0.17 to 0.21 Hz. Through a careful spectral analysis, we demonstrate that a low-temperature Compton-thick gas model characterizes the emitted radiation well. By examining the spectrum and identifying numerous absorption lines, we discerned a transition in the wind properties. This transition was marked by a shift from a state characterized by low speed, high column density, and high ionization degree to one featuring still low speed, but low column density and ionization degree. Intriguingly, the presence or absence of the QPO signal is perfectly correlated with these distinct wind characteristics. The low-speed wind observed could be indicative of a “failed wind”, while the observed shift implies a transition from a magnetically to a thermally driven wind. Notably, this QPO signal exclusively manifested itself during the magnetically driven phase, suggesting the possibility of a novel perturbation associated with magnetic effects.
The stability of adaptive full-order observer (AFO) can be improved by feedback gain tuning and speed adaptive scheme modification for sensorless induction motor (IM) drives in low-speed regenerating ...mode. However, few research concern this problem with attention to IM operating point changes. To cope with this problem, this paper proposes an operating-point tracking method to design feedback gain and speed adaptive scheme for AFO. Compared with the existing approaches, lower synchronous speed can be accessible during step speed and load changes in low-speed regenerating mode without signal injection. Addressing the operating point changes, the estimated flux error is derived by adapting the projection of estimated current error with respect to speed and torque. The derivative estimated flux error with changing parameters is taken as an additional variable to design feedback gain. Meanwhile, the speed adaptive scheme is modified by the operating-point tracking expression. As a result, the proposed method acquires the capacity to track the operating point in real time. The experimental results on a 2.2-kW IM experimental setup confirm the effectiveness of the proposed method.
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