Qinkai Han

Bio: Qinkai Han is an academic researcher from Tsinghua University. The author has contributed to research in topics: Vibration & Rotor (electric). The author has an hindex of 28, co-authored 99 publications receiving 2054 citations. Previous affiliations of Qinkai Han include Beihang University & Shanghai Jiao Tong University.

Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: A review

Abstract: As one of the most immensely growing renewable energies, the wind power industry also experiences a high failure rate and operation & maintenance cost. Therefore, the condition monitoring and fault diagnosis of a wind turbine (WT) generator set are highly needed. Among different components of a WT generator set, WT planetary gearbox plays a crucial role in transmission and leads to relatively higher failure rate and longer downtime. Towards this, a number of studies have been reported in both the academic journals and conference proceedings. This paper provides a systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection. Moreover, a few valuable open issues are pointed out and potential research directions are suggested.

Optically Levitated Nanodumbbell Torsion Balance and GHz Nanomechanical Rotor

Abstract: Levitated optomechanics has great potential in precision measurements, thermodynamics, macroscopic quantum mechanics, and quantum sensing. Here we synthesize and optically levitate silica nanodumbbells in high vacuum. With a linearly polarized laser, we observe the torsional vibration of an optically levitated nanodumbbell. This levitated nanodumbbell torsion balance is a novel analog of the Cavendish torsion balance, and provides rare opportunities to observe the Casimir torque and probe the quantum nature of gravity as proposed recently. With a circularly polarized laser, we drive a 170-nm-diameter nanodumbbell to rotate beyond 1 GHz, which is the fastest nanomechanical rotor realized to date. Smaller silica nanodumbbells can sustain higher rotation frequencies. Such ultrafast rotation may be used to study material properties and probe vacuum friction.

Bolt loosening at rotating joint interface and its influence on rotor dynamics

Abstract: The rotors of large rotating machinery involve multiple stages of disks supported by drums and shafts, where bolted joints are commonly employed to connect the adjacent disks and drums. Those connecting bolts, subjected to numerous combinations of loads during normal operation, tend to get loose, which will affect the rotor dynamics and even results in structural failures. However, little research has been done on the bolt loosening at the rotating joint interfaces of rotor systems. Thus, the influence of the bolt loosening on the rotor dynamics is studied in this paper. First, the time-varying stiffness at the joint interface with bolt loosening is investigated by means of three-dimensional (3D) nonlinear finite element (FE) models. Then, the motion equations for the rotor with bolt loosening are deduced accounting for the local stiffness variation caused by the bolt loosening. By taking a simple drum rotor with bolted joints as an example, the time-varying joint stiffness resulting from the bolt loosening and its influence on steady-state response of the rotor are calculated. The studies in this paper provide the fundamental understanding about the influence of the bolt loosening at the rotating joint interface on the rotor dynamics, and are helpful for the bolt loosening detection of rotating components in heavy-duty rotating machinery.

Analytical model of bolted disk–drum joints and its application to dynamic analysis of jointed rotor

Abstract: Bolted joints are widely used in aero-engines. One of the common applications is to connect the rotor disks and drums. An analytical model for the bending stiffness of the bolted disk–drum joints i...

Piezoelectric energy harvester for rolling bearings with capability of self-powered condition monitoring

Abstract: Rotational energy harvesting for powering low-power electronic devices and wireless sensors has attracted increasing attention in recent years. This paper proposes an energy harvester to scavenge rotational energy from rotating machines by installing an arc-shaped piezoelectric sheet between the outer race of rolling bearing and bearing pedestal. The proposed piezoelectric energy harvester cannot only supply power to sensors but also has the capability of bearing fault detection. The structural design and working principle are initially demonstrated, where an electromechanical coupling model is developed to explain the working principle of energy harvester. Then, a prototype of the energy harvester is fabricated and mounted on a rotor test rig, on which experiments are carried out to evaluate the output performance of energy harvester. The effects of rotating speed, rotor weight, shaft span and matched resistances on energy harvester performance are comprehensively evaluated. It is revealed that a single piezoelectric section of the energy harvester can generate RMS voltage of 25 V, and RMS power of 60–131 μW under the rotating speed range from 600 to 1200 r/min. Finally, the applications of the proposed energy harvester for bearing fault detection and self-powered wireless sensing are demonstrated to manifest its capability of bearing condition monitoring.

Applications of machine learning to machine fault diagnosis: A review and roadmap

Abstract: Intelligent fault diagnosis (IFD) refers to applications of machine learning theories to machine fault diagnosis. This is a promising way to release the contribution from human labor and automatically recognize the health states of machines, thus it has attracted much attention in the last two or three decades. Although IFD has achieved a considerable number of successes, a review still leaves a blank space to systematically cover the development of IFD from the cradle to the bloom, and rarely provides potential guidelines for the future development. To bridge the gap, this article presents a review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective. In the past, traditional machine learning theories began to weak the contribution of human labor and brought the era of artificial intelligence to machine fault diagnosis. Over the recent years, the advent of deep learning theories has reformed IFD in further releasing the artificial assistance since the 2010s, which encourages to construct an end-to-end diagnosis procedure. It means to directly bridge the relationship between the increasingly-grown monitoring data and the health states of machines. In the future, transfer learning theories attempt to use the diagnosis knowledge from one or multiple diagnosis tasks to other related ones, which prospectively overcomes the obstacles in applications of IFD to engineering scenarios. Finally, the roadmap of IFD is pictured to show potential research trends when combined with the challenges in this field.