The invention discloses an experimental device for simulating a bolt-connected disk-and-drum rotor of an aero-engine, belonging to the technical field of rotor dynamics. The device comprises two bearing seats installed on a test bench base, a disk-and-drum rotor is installed between the two bearing seats, a direct-current motor is arranged on the outer side of the left-side bearing seat, and the direct-current motor is installed on the test bench base through a motor seat and is connected with the disk-and-drum rotor through an elastic coupling. The body of the device is the disk-and-drum rotor which is composed of four wheel disks, two drums and two rotating shafts. The middle large-diameter wheel disk is connected with the drums on the two sides through bolts and inner flanges, the drums on the two sides are respectively in lap joint with the outer edges of the wheel disks on the outer sides, the radial outer sides are compressed tightly by two semi-circular clamps and are fixed by radial screws, the outer sides of the two wheel disks are fixedly connected with two circular-section solid rotating shafts, and the right-end rotating shaft is equipped with one wheel disk. The experimental device can be used for studying the dynamic characteristics of the disk-and-drum rotor and the transmission of vibration load in the disk-and-drum rotor.

Experimental device for simulating bolt-connected disk-and-drum rotor of aero-engine

The invention provides a bearing health assessment and prediction method and system The method comprises the following steps: acquiring a vibration signal in the running process of a bearing; processing the vibration signal to acquire time domain statistical characteristic, a time frequency domain energy characteristic and a trigonometric function characteristic of the vibration signal; selecting the characteristics which are mostly sensitive to the bearing degradation according to the characteristic index of the bearing degradation, so as to form characteristic vectors; selecting the characteristic vectors during the normal running of the bearing as input vectors to train a self-organizing characteristic mapping network and establishing a self-organizing characteristic mapping network model; solving the minimum quantization error corresponding to each characteristic vector in the self-organizing characteristic mapping network model; constructing health indexes which reflect the bearing degradation performance by utilizing the minimum quantization errors; and predicting the trend of the bearing degradation by utilizing an extreme learning machine prediction method and the health indexes According to the bearing health assessment and prediction method and system, the degradation degree in the bearing degradation process can be correctly described, and the trend of the bearing degradation can be accurately predicted

Bearing health assessment and prediction method and system

The invention discloses a reliability degree assessment method for multilevel state monitoring data fusion. The reliability degree assessment method specifically comprises the following steps of: determining states in a system and unit degradation process according to system and unit degradation laws, determining a unit state combination corresponding to the various states of a system, and collecting the state monitoring data or information of the system and units during a service process; updating the current state probability of the units in the system according to multilevel state monitoring data or information; dynamically estimating the reliability of the multi-state system in the remainder service period. According to the method disclosed by the invention, the state monitoring data or information of a system level and a unit level in the multi-state system is fused, the logical combination relationship between the system and unit degradation laws is combined, and the current state probability of each unit in the multi-state system is determined by constructing a Bayes recursive model, thus predicating the future state and reliability of the system; meanwhile, the errors of the state monitoring data or information are considered, thus the method disclosed by the invention is higher in universality.

Reliability degree assessment method for multilevel state monitoring data fusion

The invention discloses an intelligent diagnosis method for faults of a mining drilling machine gearbox. The method comprises the steps as follows: adopting an experiential mode decomposition method to decompose a mining drilling machine gearbox monitoring signal into a plurality of basic mode components, and extracting weak fault information in the signal; calculating time-domain statistical characteristic and frequency-domain statistical characteristic of each basic mode component, utilizing a distance assessment technique to select statistical characteristics sensitive to the faults, and constructing an input vector; applying a multi-classification transductive support vector machine to perform classification on the running state of the gearbox, and performing intelligent diagnosis on the various gear faults of the mining drilling machine gearbox. According to the invention, the diagnosis efficiency is high, and various weak faults of the mining drilling machine gearbox can be effectively diagnosed.

Intelligent diagnosis method for faults of mining drilling machine gearbox

The invention relates to a mechanical structure dynamic load identification method based on a cubic b-spline scaling function The method comprises the steps of (1) measuring a frequency-response function between a mechanical structure position point bearing a dynamic load and a mechanical structure response measurement point with the hammering method, obtaining a unit impulse response function through rapid Fourier inversion, and then obtaining a transfer matrix through deconvolution; (2) measuring an acceleration signal of the response point generated by the structure dynamic load through an accelerator sensor; (3) establishing scaling function matrixes, dual matrixes and coefficient matrixes of the cubic b-spline scaling function under different layer numbers, and selecting a coefficient matrix with the minimum conditional number; (4) calculating the weight coefficient of the cubic b-spline scaling function under the regularized layer number by means of an acceleration response and coefficient matrix; (5) obtaining a regularized mechanical structure dynamic load to be identified by multiplying the weight coefficient by the corresponding scaling function matrix

Mechanical structure dynamic load identification method based on cubic b-spline scaling function

The invention discloses a second generation small-wave support vector machine assessment method for damage and remaining life of a metal structure. According to the method, an intrinsic mode function is obtained through decomposition of an experience mode, a time-frequency domain statistic characteristic of the intrinsic mode function is extracted, a most sensitive characteristic is chosen according to a distance accessment principle to construct a best characteristic set, a minimum quantization error indicator which has an obvious performance degradation trend along with time changes is constructed by means of self-organization neural network characteristic fusion techniques, a biorthogonal small-wave support vector machine kernel function of second generation small-wave transform is provided, a service life prediction model of the second generation small-wave support vector machine is constructed, the minimum quantization error indicator serves as a prediction characteristic, and quantitative assessment for the damage and the remainig service life of a metal structural component of mechanical equipment under a small subsample is achieved.

Second generation small-wave support vector machine assessment method for damage and remaining life of metal structure

The invention discloses a method for judging the assembling reliability degree of a bolt connection device with state information. According to the method, firstly, the state information of the device is acquired through a motivation test; then, the acquired state information is preprocessed through a feature extraction method to obtain a state feature matrix; the inherent mode of the feature matrix is analyzed through a mode recognition algorithm, and a state subspace corresponding to the state feature matrix is constructed; the similarity index of a normal state subspace and the current state subspace is calculated, and the index reflects the similarity of the two state subspaces, and can describe the closeness degree of the device performance at the current moment and a normal state; finally, the similarity index is used as a reliability index to quantitatively reflect the assembling reliability degree of the device. The method does not depend on a large number of failure samples, is simple and easy to implement, has the advantages of being reliable in result, strong in practicality and the like, is suitable for evaluating the assembling reliability degree of the bolt connection device in real time on site, and has engineering application value.

Sun Chuang

Papers

Second generation small-wave support vector machine assessment method for damage and remaining life of metal structure

Method for judging assembling reliability degree of bolt connection device with state information

Method for determining assembly reliability of detachable disc-drum rotor