To overcome the destructive nature and online-monitoring limitations of traditional hardness testing, this paper proposes a non-destructive hardness evaluation method for ferromagnetic materials by fusing Magnetic Incremental Permeability (MIP) and Magnetic Barkhausen Noise (MBN). A dual-modal detection system was constructed for synchronous excitation and acquisition of MIP and MBN signals. Multi-dimensional electromagnetic features were extracted from the time domain, frequency domain, time-frequency domain, and butterfly plot geometry to characterize material hardness. To reduce feature redundancy, a Dynamic Weighted Recursive Feature Elimination (DW-RFE) algorithm was developed using multi-model ensemble learning, dynamic weight allocation, and stability evaluation. Experimental results show that the DW-RFE-based model achieves average relative errors of 2.30% on the complete dataset and 2.87% on the incomplete dataset, demonstrating good accuracy and generalisation ability for non-destructive hardness evaluation.

To achieve non-destructive hardness testing of ferromagnetic materials, this study develops a bimodal detection system capable of synchronously exciting and acquiring magnetic incremental permeability (MIP) and magnetic Barkhausen noise (MBN) signals, from which a 38-dimensional multi-domain feature set is extracted, encompassing time-domain, frequency-domain, and butterfly diagram geometric features. To address the redundancy and noise problems inherent to high-dimensional features under small-sample conditions, a Dynamic Fusion Adaptive Recursive Feature Elimination (DFARFE) algorithm is proposed, which integrates a multi-criteria weighted evaluation framework, Bootstrap resampling-based dynamic weight allocation, and leave-one-out cross-validation. The resulting feature subset is highly correlated with hardness while exhibiting excellent interference resistance. Experimental results demonstrate that the multi-parameter regression model constructed upon this subset attains an average relative error of only 0.78% on the test set. Compared with conventional methods, the feature subset selected by the proposed approach possesses stronger interference resistance and generalization capability, thereby significantly enhancing both the accuracy and robustness of hardness evaluation for ferromagnetic materials.

To address the critical challenges prevalent in weak capacitance detection, such as sensitivity to parasitic parameters, DC baseline drift interference, and frequency non-synchronization errors, this paper presents a high-precision weak capacitance measurement system based on a frequency self-estimation orthogonal lock-in amplifier (FSE-OOLIA). In terms of the analog front-end design, the paper proposes a damped double-feedback structure and a parasitic parameter cancellation technique based on the Miller compensation principle. This approach effectively mitigates the risk of operational amplifier self-oscillation under large capacitive loads at the hardware level and significantly suppresses parasitic capacitance effects from input terminals and leads, achieving a wide dynamic measurement range from 1 pF to 470 & micro;F. Furthermore, by incorporating a specialized DC servo loop, real-time active suppression of input leakage current is achieved, substantially enhancing system robustness in non-ideal insulation environments. In the digital signal processing domain, the FSE algorithm is employed to algorithmically eliminate spectral leakage and demodulation errors caused by clock asynchrony between the transmitter and receiver. Furthermore, the Allan variance is introduced as a core metric for evaluating system noise and resolution, precisely quantifying the system's long-term frequency stability. Experimental results demonstrate that the system maintains a relative measurement error within 1% across an excitation frequency range of 0-100 MHz, achieves an ultimate capacitance resolution of 4.7 aF, and simultaneously supports an ultra-wide dynamic range from 1 pF to 470 & micro;F with an excitation bandwidth up to 100 MHz. The combination of competitive resolution, wide dynamic range, and high excitation bandwidth provides a high signal-to-noise ratio universal solution for the precise detection of weak signals in applications such as micro-electro-mechanical systems sensors and microfluidic chips.

Engine blades, being critical components of aircraft engines, pose a substantial threat to both the engine and the entire aircraft if they fracture during flight. Hence, inspecting and maintaining these blades are crucial to ensuring flight safety. In the process of blade damage detection, personnel typically utilize borescope inspection equipment to manually examine each blade and count them as they pass, thereby guaranteeing the examination of every individual blade within the engine to prevent any missed or duplicate inspections. This paper presents a new video interpretation method applied to the scenario of engine blade counting. The core of this algorithm involves employing the cosine correlation function to calculate the similarity between video frames captured during borescope inspections, followed by adaptively thresholding the processed signal for dynamic binarization, and ultimately counting the falling edges. By adopting frame-related approaches instead of relying on local image characteristics, this algorithm exhibits high robustness against smooth blade surfaces and metallic reflections. Additionally, it efficiently manages motion blur and directional variations that occur during the rapid movement of the blades. Compared to existing methods, this algorithm requires minimal training time, is compatible with various turbine engine blades, and guarantees real-time count updates.

Our research introduces a novel stochastic resonance (SR) model featuring a single potential well and develops a dedicated detection system designed to address the challenging problem of detecting impact signals within a highly noisy background. We begin by examining the limitations of conventional metrics, such as the cross-correlation coefficient and kurtosis index, in identifying nonperiodic impact signals, and subsequently introduce an improved metric. By harnessing parameter-adjusted SR, this innovative potential well model and metric is integrated to formulate an adaptive detection method for nonperiodic impact signals. This method automatically adjusts system parameters in response to the input signal. Subsequently, numerical simulations of the system is conducted so as to perform a comparative analysis with experimental results obtained from both asymmetric single potential well and periodic potential systems. Our findings conclusively demonstrate the enhanced effectiveness of our proposed method in detecting impact signals within a high-noise environment. Furthermore, the method provides more accurate estimates of both the intensity and precise location of the input impact signal from the output results.

Rolling contact fatigue (RCF) cracks online detection using eddy current testing (ECT) is in urgent demand. However, RCF cracks detection and evaluation in this way under moving conditions remains challenging due to the velocity effect in ECT. This paper aims to study the response of a transmitter-receiver eddy current probe to cracks under moving conditions and evaluate the depth and inclination angle of RCF cracks. In this paper, a high-speed eddy current testing system is developed to experimentally investigate the influence of coil gap, detection speed, and the lift-off on the eddy current probe's response under moving conditions. In addition, the temporal and amplitude features of the eddy current signal are extracted to characterise the depth and inclination angle of the RCF cracks. The experimental results indicate the eddy current probe's response can be improved by increasing the coil gap (coil centre distance) suitably, which can be done to compensate for the attenuation of the eddy current signal caused by detection speed and lift-off. The probe's response hardly changes with an increase in detection speed when the driver and pick-up coils of the eddy current probe completely overlap. The crack depth and inclination angle can be evaluated under moving conditions.

In this paper, in order to meet the engineering requirements of mechanical properties detection of materials with low power consumption, a novel incremental permeability detection system based on permanent magnet excitation is proposed to reduce system power consumption. Initially, a new incremental permeability sensor was designed to extract magnetic incremental permeability (MIP) signal. Through finite element simulation, experimental parameters were optimised, and a rational arrangement of double permanent magnets was devised to furnish an optimally strong AC bias field intensity. Subsequently, a practical detection platform was assembled to extract electromagnetic signal features. From the perspective of domain wall displacement, a characteristic value theta was proposed to predict the yield strength, with experimental results yielding a relative error of less than +/- 10%. These experiments have validated the capability of permanent magnet-type MIP to enable quantitative detection of the yield strength in ferromagnetic materials.

In this paper, magnetic Barkhausen noise (MBN) and tangential magnetic field (TMF) are employed to quantitatively predict the hardness of bearing steel GCr15. In order to solve the problems that MBN and TMF signals are susceptible to electromagnetic interference (EMI) and sensor vibration during the inspection process, which lead to the decrease of the hardness prediction accuracy, a feature-based abnormal signal elimination algorithm is proposed. The features of MBN and TMF signals are used to determine whether the signals are affected by EMI or sensor vibration. To verify the effectiveness of the algorithm, the multiple linear regression (MLR) and multilayer perceptron (MLP) hardness prediction model are developed based on MBN and TMF features. After removing abnormal signals, the hardness prediction error of MLR model is reduced from 21.39 to 1.25% and the hardness prediction error of MLP model is reduced from 7.75 to 0.13%.

The incremental permeability (IP) is related to the reversible domain-wall motion of ferromagnetic materials during the period of dynamic magnetization. IP feature γ can be used to estimate the yield strength of materials. However, the IP signal generated by reversible domain-wall motion is weak. During the experiment, the vibration of the platform and the uneven surface of the sample will change the lift-off conditions of the probe, resulting in the distortion of the IP signal. In order to solve this problem, in this paper, a two-parameter fitting method is proposed to optimize the estimation of yield strength by γ. Two compensation parameters a_γ and b_γ related to lift-off are proposed to modify the γ curve, making it possible to detect the material under different lift-off conditions. Finally, the yield strength of the material is estimated under different lift-off conditions, and the estimation error is less than 10

针对滚动轴承在实际工作环境中噪声较大和负载变化的问题,提出一种基于双注意卷积机制的残差神经网络(DACM_ResNet,double attention convolution mechanism ResNet)轴承故障诊断方法;首先,对滚动轴承振动信号进行短时傅里叶变换(STFT,short-time fourier transform)并使用伪彩色处理得到三通道图像数据;然后,对残差神经网络在轴承故障诊断上进行研究,在残差单元的卷积层之后,使用DACM模块,将残差特征在通道和空间维度上进行进一步提取,最后,在凯斯西储大学(CWRU)数据集上进行试验验证,试验结果表明所提出的方法在噪声环境下及负载变化时,平均诊断准确率达到了98％以上,说明所提出的模型有较好的鲁棒性.

In this paper, magnetic Barkhausen noise (MBN) is employed to quantitatively predict the hardness of GCr15 bearing steel. Firstly, to thoroughly investigate the relationship between MBN signal features and material hardness, a comprehensive study is conducted on multi-feature extraction methods for MBN signals based on time domain, frequency domain and time–frequency domain. Secondly, a novel feature evaluation algorithm is proposed that considers the correlation, stability and discriminability (CSD) of MBN features. This algorithm selects MBN features that are relevant to material hardness, remain stable under the same hardness level, and can distinguish between different hardness levels. Finally, linear regression models and multilayer perceptron models are established for the relationship between MBN features and material hardness. The models built using the features selected by the CSD feature evaluation algorithm demonstrate superior accuracy, with the root mean square error of 1.04 HRC for predicting unknown hardness values.

Typical Incremental permeability (IP) requires a high power supply to generate a bias magnetic field that periodically magnetizes the testing samples, making it difficult to apply to occasions where high power supply is short. To solve this problem, this paper presents a low power NDT method based on IP, which is characterized in that the permanent magnets are used to form a bias magnetic field, and the sample is magnetized aperiodically by moving the permanent magnet in one direction. Finite element simulation is used to compare the magnetic fields formed by permanent magnets with different number of blocks and distance between them, as well as the hysteresis curves under magnetic excitation, and a finite element simulation method (FEM) of IP is given. Our experiments used the new method to estimate the yield strength of steels, demonstrating that the proposed method can obtain an approximately accurate result, and its power consumption is less than 2 W.

Eddy current testing(ECT) is widely applied to detect surface defects on metallic materials. In ECT for cracks on complex curved surfaces, immunity to lift-off and the detection ability of the eddy current probe are critical challenges. In this study, we designed and developed a novel differential transformer ECT probe using AD698 signal conditioning to detect cracks with different orientations in rail treads. It adopts flexible printed circuit board (FPCB) technology for complex curved surfaces. The new ECT probe consists of four-square driver coils and two 8-shaped pick-up coils with antiserial connections that can sense opposite magnetic field changes in different directions. The proposed probe can solve the detection ability and lift-off problem of crack with different orientation on complex curved surfaces. The theoretical analysis which is based on transformer principles, is described in this paper. The detection ability of the new probe at different frequencies was evaluated by numerical simulation. Upon completion, the numerical simulations analyzed the effects of crack with different orientation and lift-off on the detection ability of the proposed probe. The experimental system was built to verify the detection ability of different crack with different orientations and immunity to lift-off by the proposed probe. The results of the simulation and experiment indicated that the proposed probe has a good ability to detect crack with different orientation whereas its performance decreases as crack orientation increasing. The proposed probe can also substantially suppress lift-off noise.

In this paper, a simple singlemode-multimode-singlemode (SMS) optical fiber sensor is proposed and investigated for measuring the air pressure of a circular thin plate. Theoretical analysis and experimental demonstration are presents in this paper. The air pressure changes the strain and bending radius of the SMS sensor directly attached to the thin plate, and hence leads to shifts and intensity variation of the transmission spectrum. The relationship between transmission spectrum and pressure has studied by finite element method and optical simulation analysis. Experimental results show that the intensity-pressure and wavelength-pressure are -0.2718 dB/kPa and-106.7 pm/kPa in a pressure range of 70-130 kPa, respectively. The repeatability of the sensor is good, and the hysteresis rate is low. The key features of the proposed sensor are its simple structure and manufacturing process, low manufacturing cost. It can be used for high-precision pressure measurement.

In the current production of iron and steel industry, the testing of mechanical properties of ferromagnetic materials relies on tensile testing, which is time-consuming and destructive, thus greatly increasing the production cost. In order to solve this problem, a method based on pulsed eddy current is proposed to estimate the yield strength of ferromagnetic materials. Eddy current loss and hysteresis loss are the sources of loss in the magnetization process of ferromagnetic materials. Not only the loss but also the yield strength of the material is related to the microstructure of the magnetized material. In this paper, the relationship between loss in the process of magnetization and microstructure of materials is analyzed. The features of eddy current loss and hysteresis loss were found from the pulsed eddy current signals, and the yield strength evaluation model was established by feature fitting. The experimental results show that the model has high evaluation accuracy.

针对基于稀疏表示(Sparse representation,SR)的数据压缩压缩率低、重构精度低等问题,本文提出一种基于双迭代的聚能量字典学习算法,把高维信号映射到低维特征空间,当低维特征空间保留高维原始信号越多的特征时,高维信号从低维特征空间中恢复出来的精度越高.为了使低维字典保留高维字典更多的主成分,本文提出了一个新的变换,被命名为?变换,能提升高维字典的能量集中性.除此之外,针对高维字典与低维字典的耦合关系,建立了双循环迭代训练,增加字典的能量集中性与字典的表达能力.实验表明,相比于传统算法,本文提出算法字典学习收敛速度提升了3倍以上.此外,该方法可以得到较高的压缩比和更高质量的重构信号.

数字电路的亚稳态现象会导致数据发生误码,同步寄存器链常常被用于降低亚稳态发生的概率.为了量化由亚稳态导致的数据误码发生概率,本文从亚稳态产生的本质出发分析了亚稳态在同步寄存器链中传递的原因;推导了考虑线延迟与逻辑门延迟影响的精确亚稳态稳定时间公式;设计了一种新亚稳态测试电路计算三种亚稳态输出结果发生的概率.在平均故障时间参数的基础上,计算了因为亚稳态而造成的同步寄存器链误码率和整个系统的误码率,给出了降低系统误码率的措施.

The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

Ferromagnetic material is one of the basic materials in industrial production and whose mechanical properties are key indicators to evaluate quality. At present, incremental permeability (IP), one method of nondestructive testing, is used in the mechanical properties testing field of ferromagnetic materials, but its 3D simulation prediction model and method have not been established. To solve this problem, a finite element simulation method for mechanical properties of ferromagnetic materials based on Jiles-Atherton (JA) hysteresis model was proposed in this paper, which is established on the basis of the experimental method of electromagnetic nondestructive testing via incremental permeability. In this paper, the finite element 3D simulation prediction model based on IP method is proposed. Besides, the difficulty in JA hysteresis model parameter measurement is solved by combining magnetic-force method and artificial fish swarm algorithm. The 3D simulation prediction model is applied to the yield strength prediction of ferromagnetic materials for the first time. By testing the yield strength of the material, the simulation method and 3D simulation prediction model proposed in this paper can accurately reflect the incremental permeability characteristics of real materials and can provide a simulation platform for testing the mechanical properties of ferromagnetic materials via IP method.

高速载运设施事关国家命脉,在其生产、布设、使用和维护各个环节,都需无损检测技术保驾护航.本文就高速载运设施无损检测的必要性,检测现状、检测技术的发展趋势(包括基于多物理原理的新型伤损检测技术,材料应力、微观结构、机械性能等状态检测,以及无损检测和结构健康监控及人工智能的结合)进行综述,旨在为高速载运设施的无损检测打下基础.