Iet Science Measurement & Technology最新文献

Faults in rolling bearings are usually observed through pulses in the vibration signals. However, due to the influence of complex background noise and interference from other machine components present in measurement equipment, vibration signals are typically non-stationary and often contaminated by noise. Therefore, in order to effectively extract the random variation and non-linear dynamic variation characteristics of vibration signals, a new method of rolling bearing fault diagnosis based on generalized multiscale mean permutation entropy (GMMPE) and grey wolf optimized least squares support vector machine (GWO-LSSVM) is put forward in this paper. Based on the multiscale permutation entropy (MPE), the multiscale equalization is firstly used to replace the coarse grained process, and the value of mean is extended to variance to avoid the dynamic mutation of the original signal. Finally, the parameters of LSSVM are optimized by the grey wolf optimization algorithm to achieve accurate identification of fault modes. The results of simulation and experiment show that applying the proposed GMMPE to rolling bearing fault feature extraction is feasible and superior, and the method based on GMMPE and GWO-LSSVM has better noise robustness, which can effectively achieve rolling bearing fault diagnosis.

In this paper, an improved two-step method is presented for the sensitivity analysis of vector network analyzer (VNA) S-parameter measurements due to the non-ideal line-reflect-match (LRM) calibration standards. This improved method is based on the indirect uncertainty propagation mechanism, which is especially suitable for the S-parameter measurements applying the self-calibration technique. To further simplify the formula derivation, the deviation matrices [δA] and [δB] are newly defined to represent the uncertainties of the T-matrices of error boxes. With this definition, formulas for the deviations of device under test (DUT) S-parameters can be concluded as functions of [δA] and [δB] in a concise form. Eventually, by solving only three linear combinations of the elements from [δA] and [δB], the sensitivity coefficients of DUT S-parameters due to non-ideal LRM can be conveniently deduced in an analytical form. Finally, experiments are performed to verify the proposed method.

The converter transformer is an essential part of the DC transmission system. Compared with the traditional oil-impregnated AC transformer, the main insulation of the converter transformer bears more complex electric field aging stress during long-term operation. The influence of the proportion of AC components in the AC/DC composite electric field on insulation aging is still unclear. Therefore, a combined aging test platform of composite electric field and thermal was built in the laboratory, and accelerated aging tests of oil-paper insulation under different AC/DC ratios were carried out. Through the time-frequency domain dielectric response characteristics of oil-paper insulation, the quantitative relationship between the time-frequency domain dielectric response characteristic parameters and AC proportional coefficient in different aging stages was obtained. The results show that the influence of the AC component on the aging of the oil-impregnated pressboard is more prominent. The maximum relaxation polarization time and the maximum exponential coefficient of polarization–depolarization current (PDC) can effectively characterize the aging of oil-paper insulation. Meanwhile, to accurately assess the insulation state of the converter transformer, this paper established the equivalent dielectric relaxation model for the main insulation structure. A quantitative assessment method for moisture content and aging of oil-paper insulation based on time-frequency domain dielectric response was proposed. The influence of transformer oil conductivity, test temperature, and main insulation structure was eliminated. The effectiveness of this method was verified by comparative tests, the maximum error for DP is 20, and the maximum error for moisture content is 0.15%. The research results of this paper can provide theoretical support for on-site assessment of converter transformer insulation status.

Partial discharge (PD) signals have a large amount of data and a low energy proportion of pulse signals, resulting in difficult data transmission and poor reconstruction efficiency. To this end, a PD signal compression reconstruction method based on transfer sparse representation and dual residual ratio threshold (TSR-DRRT) is proposed. TSR-DRRT is centred on the sparse representation (SR) and accurate reconstruction of noisy signals. The intrinsic pulse of PD signals is extracted by signal decomposition, and jointly trained with different types of signals to establish the transfer SR dictionary. The compressed signal accurately retains the essential characteristics of the pulse information by improving the match between the dictionary atoms and the polymorphic PD pulses. To match the transfer SR dictionary, the inner and outer DRRT iteration termination conditions are set adaptively during the reconstruction process based on the correlation difference between the dictionary and signal frames. Independent control of PD pulse recognition and reconstruction accuracy is achieved, and its performance under noisy signals is improved. The results show that the method can achieve high ratio compression and efficient reconstruction of noisy signals. Different types of PD signals can also have high matching accuracy. This method can meet the demand for PD signals compression and transmission to the terminal for accurate reconstruction.

Air-insulated switchgear plays an important role in distribution network and partial discharge (PD) is one of the common faults in its operation. The method of gas component analysis, which is to detect chemical gas products, is a novel one to discover PD. In order to study the feasibility of the method, the monitoring experiment of characteristic gases, comprising carbon monoxide (CO), nitrogen dioxide (NO₂), and ozone (O₃), of metal protrusion PD is carried out. This paper conducts the comparative experiments on three main factors affecting PD: voltage, air humidity and degree of protrusion, as well as studies how they affect the formation of characteristic gases. According to the experimental result, higher applied voltage promotes the formation of characteristic gases in the same conditions. In addition, with the increase of humidity from 40% to 75%, the generation of gases is inhibited. Furthermore, when the needle–plate distance is 30 mm, the gas concentration has almost no change within 6 h, which is different from that of 10 mm. In conclusion, the difference in gases concentration reveals the influence of three main factors on the generation of characteristic gases and provides an analytical basis for PD detection by gas composition analysis.

Hong-shan, Z., Xiao-mei, G., Li-bo, M., Yan, W., Cheng-yan, S., Jie-ying, C.: A hierarchical diagnosis method of cable aged segment based on transfer function. IET Sci. Meas. Technol. 16, 512– 522 (2022). https://doi.org/10.1049/smt2.12125

In Section 0 Introduction, we missed an important reference ‘Wu Y , Zhang P , Lu G . Detection and location of aged cable segment in underground power distribution system using deep learning approach. IEEE Transactions on Industrial Informatics, 2021, 17(11):7379-7389.’ because of our mistakes. The code in this paper is developed based on the reference and we further make hierarchical diagnosis and consider the influence of cable joints to improve the sensitivity and accuracy of cable aging diagnosis.

We apologize for this error.

Here, performance of auto-encoder deep neural networks in detection and isolation of induction motor states (healthy, bearing outer race fault, stator winding short circuit and broken rotor bar) in the presence of unbalanced power supply and electro-pump dry running disturbances is investigated. Easily available three-phase electrical current signals are denoised using independent component analysis, and then the frequency-domain signal is used to train a neural network. A comparison is made between shallow and deep neural networks and also between the conventional structure of deep methods and the encoder–decoder structure in terms of training and test accuracy and robustness. In fact, the depth is increased and the effectiveness is investigated. At the end, it is shown that an encoder–decoder structure leads to the best result in terms of accuracy and robustness. The algorithms are examined experimentally, and the results show that the auto-encoder deep neural network can detect the aforementioned faults with a high reliability in the presence of disturbances.

An adaptive Dynamic Surface Controller (DSC) is designed for a two-axis gimbal system with actuator dynamics in the presence of parametric uncertainties in [1]. A Lyapunov stability analysis is used to guarantee the convergence of the tracking error to the origin and boundedness of all closed-loop signals. The main objective of this corrigendum is to point out several errors that occurred throughout the paper, resulting in the inaccuracy of the used dynamic model and ineffectiveness of the proposed controller. It should be noted that taking into account the corrections stated in this corrigendum, the main result of the original paper is still valid.

Rectal cancer is one of the most common lower gastrointestinal diseases worldwide. Currently, the common treatment is low anterior resection (LAR) of the rectum, which preserves the anus of the patient. However, it is easy to cause low anterior resection syndrome after surgery, which has a significant negative impact on the life of patients, and there is no unified evaluation standard for postoperative rectal function. To solve this problem, a multi-sensor fusion rectal information acquisition system is designed in this paper, and a rectal signal processing method is proposed to theoretically evaluate the rectal function of postoperative patients. The method uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the one-dimensional rectal signal to solve the underdetermined ICA problem, uses the Fast independent component analysis (Fast-ICA) to separate the pure rectal signal, uses the wavelet packet to extract features, and uses the particle swarm optimization optimizes support vector machine (PSO-SVM) to classify and evaluate postoperative function. According to the experimental results, the rectal signal preprocessing effect is good, the evaluation prediction rate is 99.5565%, and the algorithm classification results are accurate, which provides a certain preliminary theoretical basis and reference value for the evaluation of rectal function after LAR.