Effective fault diagnosis is an important guarantee for the safe and stable operation of mechanical systems. Nowadays, most fault diagnosis methods are for known classes. However, the unknown fault usually occurs in the process of mechanical operation and may be wrongly divided into defined classes, which increases the risk of mechanical shutdown and the difficulty of maintenance. Therefore, to achieve high-accuracy diagnosis with unknown fault, a method based on transfer learning and deep transfer clustering (DTC) is proposed, named transfer clustering calculation center points (TCCP). TCCP completes the task of fault diagnosis with an unknown class by calculating the feature space distribution between the sample to be tested and the known samples and further improves the accuracy by matching the target distribution. In two open-source bearing vibration datasets and an acoustic emission (AE) dataset of the self-made bearing, five bearing diagnostic models are used to compare the unknown failure diagnosis. The average accuracy with unknown failure by TCCP is 97.08%, 89.78%, and 95.24%, respectively. The accuracy is 24.45%, 23.16%, and 10.08% higher than the average accuracy without TCCP. TCCP can get better diagnostic results under both vibration signals and audio signals. TCCP has predominant universality and generalization capabilities and provides an effective approach for the identification of unknown failure.

Measurement error occurs if the requirements of undistorted measurements cannot be satisfied by dynamic component characteristics of the sensor. Amplified time constant and longer thermal response time of armored thermocouple is caused by its additional protective cover structure and the inherent thermal inertia of the internal temperature measuring element. Therefore, there is hysteresis dynamic characteristic in actual measurement of sensor. This paper proposes an accurate compensation of armored thermocouple based on fireworks algorithm that balances the contradiction between the accuracy of the existing solution and the complexity of the solution, corrects the measured thermocouple temperature signal. Weight factor is innovatively introduced to design the fitness function of fireworks algorithm, and the iterative process is sectionally executed that achieves the improvement of time constant and thermal response time of armored thermocouple. Finally, results are given in the simulation section. Dynamic compensation time is reduced from 537ms to 130ms, which is 75.79% lower than before compensation result. Moreover, the thermal response time is reduced from 345ms to 110ms, which is 68.16% lower than the before compensation result. From the experimental results, we can observe that the proposed method in this paper can broaden the working bandwidth of the thermocouple sensor through establishing dynamic mathematical model of thermocouple. After compensation, the dynamic characteristics of armored thermocouple are close to bare bead thermocouple without protective sleeve. The proposed method can replace easily damaged bare bead thermocouple to realize accurate measurement in harsh temperature measurement environments without need frequent replacement, which also improves economic benefits.

Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain: In the process of transient test, due to the insufficient bandwidth of the pressure sensor, the test data is inaccurate. Firstly, based on the projection of the shock tube test signal in the sparse domain, the feature expression of the signal sample is obtained. Secondly, the problem of insufficient bandwidth is solved by inverse modeling of sensor dynamic compensation system based on swarm intelligence algorithm. In this paper, the method is used to compensate the shock tube test signals of the 85XX series pressure sensors made by the Endevco company of the United States, the working bandwidth of the sensor is widened obviously, the rise time of the pressure signal can be compensated to 12.5μs, and the overshoot can be reduced to 8.96. The repeatability of dynamic compensation is verified for the actual gun muzzle shock wave test data, the results show that the dynamic compensation can effectively recover the important indexes such as overpressure peak value and positive pressure action time, and the original shock wave signal is recovered from the high resonance data.

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.