Xinyu Wang

Oil debris monitoring is an important and effective method for evaluating the wear conditions of mechanical equipment, therefore providing early fault warnings. In particular, inductive sensors sense the size and distribution of wear debris in oil lubrication by extracting features from induced voltages. However, in practice, induced voltages generated by wear debris are inevitably contaminated by random noise and interference, so denoising operation is usually required. In this paper, the sensing model of high-gradient magnetostatic field-based debris detection sensors was re-investigated. A new mathematical model was established, based on which a correlation-metric-based adaptive target signal localization strategy and a new indicator quantitatively analyzing the size of debris were proposed. Additionally, according to the established mathematical model, the numerical characteristics of target signal were extracted to achieve the identification, counting, and quantitative analysis of wear debris. The oil experiments validated the derived signal model and demonstrated the effectiveness and robustness of proposed algorithm in feature extraction, identification, and quantitative characteristic analysis.

Monitoring the wear debris in lubricant systems is an effective method of reflecting the health of mechanical equipment. With the advantages of being simply structured, noninvasive, and insensitive to oil quality, inductive sensors are often deployed for debris detection. However, induced voltages generated by wear debris are usually contaminated by noise and other undesired components, thereby limiting the reliability and availability of the sensors. In this article, a new debris-detection framework is proposed based on an inductive sensor with parallel dual coils. With the aid of a reference signal, a multi-least-mean-square adaptive weighting filtering method was developed, and good noise suppression was achieved with little violation of the debris signatures. The algorithm is illustrated through numerical simulations, and the effectiveness of the proposed framework was verified by an oil experiment. Two traditional denoising algorithms were also analyzed for comparison. The experimental results demonstrated that the proposed strategy had an excellent capability for extracting and identifying debris features.

In this work, we propose a novel coarse-to-fine method for object pose estimation coupled with admittance control to promote robotic shaft-in-hole assembly. Considering that traditional approaches to locate the hole by force sensing are time-consuming, we employ 3D vision to estimate the axis pose of the hole. Thus, robots can locate the target hole in both position and orientation and enable the shaft to move into the hole along the axis orientation. In our method, first, the raw point cloud of a hole is processed to acquire the keypoints. Then, a coarse axis is extracted according to the geometric constraints between the surface normals and axis. Lastly, axis refinement is performed on the coarse axis to achieve higher precision. Practical experiments verified the effectiveness of the axis pose estimation. The assembly strategy composed of axis pose estimation and admittance control was effectively applied to the robotic shaft-in-hole assembly.

A new stress measurement method using single source high-frequency cylindrical guided waves is put forward to promote the efficiency and precision of the contact-type ultrasonic testing technique. The propagation patterns and acousto-elasticity of cylindrical guided waves, traveling in a loaded steel rod, are analyzed under the theoretical framework of the Pochhammer-Chree equation and nonlinear acoustics. According to the theoretical derivations, the ratio of the maximum group velocities of different modes is selected as the measurement criterion of stress. Then, a stress calibration/measurement experiment is conducted to prove the validity of the method. A single-source ultrasonic transducer driven with an impulsive excitation is introduced in the experiment and the generated multimode guided waves in the received signals are analyzed. It is observed from experimental results that the group speed ratio of two different modes is linearly proportional to the axial stress and the evaluation errors of the proposed method is less than 6%.

The detection of wear condition is critical for the prediction of mechanical equipment failure and the avoidance of catastrophic accidents. A large-diameter inductive debris detection sensor was previously presented to satisfy the requirement of high throughput, and the characteristics of induced voltage generated by single debris were analyzed. However, in practice, plenty of wear particles carried by lubricant flowing through the sensors probably form the aliasing signal, thus affecting the evaluation of concentration and size of particles. In order to analyze the mechanism of aliasing, this paper studies the influence of the motion state of wear debris, such as velocity, relative spatial position and trajectory, on the waveform of the aliasing induction signal. The experimental results show that the aliasing induced voltage is proportional to the debris velocity and magnetic induction intensity. Additionally, the voltage peak-peak of aliasing signal is approximately proportional to the radius of the debris instead of the volume, from which the relational expression about the aliasing signal and component singles derives. Finally, the theoretical results and experimental results are performed to validate this relation.

Wear is one of the main factors of machine failure. If abnormal wear was not detected in time during the operation of a mechanical system, it probably leads to catastrophic consequences. The wear debris in the lubricating oil circuit contains much information about equipment wear. Consequently, debris detection is regarded as an effective way to detect mechanical faults. In this paper, an inductive debris sensor based on a high-gradient magnetic field is presented for high-throughput lubricating oil circuits. The excitation coil of the sensor is driven by a constant current to generate a high-gradient magnetic field, and the induction coil is wound around the flow path. When wear debris cuts the magnetic line through the flow path, a corresponding induced voltage is generated. The experimental results show that the sensor output signal is linear with the drive current and the wear debris velocity. In addition, the shortest distance between the particles that the sensor output signals can be completely separated is 25 mm. When the distance is smaller, the induced signals are superimposed.