On-line diagnosis of slight interturn short-circuit fault (ISCF) has always been difficult in low-speed permanent magnet synchronous motors (PMSMs). It is found that the residual zero-sequence voltage exists in healthy PMSMs due to the inherent unbalance of a low-speed motor. This paper proposes a new ISCF diagnosis method based on zero-sequence voltage vector decomposition to solve this problem. The voltage vector whose phase angle is closest to ISCF phase angle is extracted from zero-sequence voltage vector, which improves the sensitivity of fault phase identification. Furthermore, the method of extracting phase voltage from PWM duty cycle control voltage is proposed, which can reduce the error caused by measuring zero-sequence voltage with three-phase balanced resistance. Experiments demonstrate that the on-line detection limit of ISCF is reduced from 5% to 1% (short-circuit ratio) for a low-speed PMSM.

Compared with using a single characteristic parameter of electrochemical impedance spectroscopy (EIS) to classify the freshness of fish samples from different origins, more characteristic parameters could bring higher accuracy as well as complexity, subjectivity, and uncertainty. In order to eliminate the disadvantages of the multiparameter model, a data fusion method based on model similarity (DFMS) was proposed in this study. The similarity relation between the freshness models based on EIS characteristic parameters and physicochemical indicator was analyzed and quantified accordingly, and then, the weighting factors of the fusion model were determined. The classification accuracy rate of fish freshness based on DFMS was 9.2∼15% greater than that of a single EIS characteristic parameter. The novel dimensionless fusion parameter method proposed in this article might provide a simple yet effective indicator for EIS-based food quality evaluation.

Tubular permanent magnet motor (TPMM) can offer great thrust force density and high efficiency. To enhance its robustness against disturbances and solve the contradiction between response time and overshoot, a novel sliding mode control strategy based on disturbance observer (DOB-SMC) was proposed for a TPMM velocity drive system. The novelty of the proposed velocity control strategy lies in the incorporation of the mismatch disturbance observer in the sliding surface and the sliding mode control (SMC) law, and the introduction of internal model control (IMC) into the SMC law. The velocity control system is transformed to a first-order inertia model by IMC, thus the dynamic response performance is improved. The mismatched disturbances are estimated, and then the effect of mismatched and matched disturbances can be restrained with little chattering. The simulated and experimental results are presented as validation for the proposed strategy.

The increasing attention to precision medicine is widely paid to greatly rise the cure rate of cancer. Improving the stability and accuracy of cancer cell viability evaluation is one of the keys for precision medicine, as excess dosage of anti-cancer drugs not only kills the cancer cells, but also does harm to normal cells. Electrochemical impedance sensing (EIS) method is well known as a label-free, non-invasive approach for real-time, online monitoring of cell viability. However, the existing EIS methods using single-frequency impedances cannot reflect the comprehensive information of cellular impedance spectroscopy (CIS), ultimately leading to a poor stability and low accuracy of cancer cell viability evaluation. In this paper, we proposed a multi-frequency approach for improving the stability and accuracy of cancer cell viability evaluation based on multi-physical properties of CIS, including cell adhesion state and cell membrane capacitance. The results show that the mean relative error of multi-frequency method is reduced by 50% compared with single-frequency method, while the maximum relative error of the former is 7∼fold smaller than that of the latter. The accuracy of cancer cell viability evaluation is up to 99.6%.