Interpretation: A centrifuge is a machine that uses centrifugal force to separate liquid and solid particles or components of a mixture of liquid and liquid. The special ultra-speed tubular separator can also separate gas mixtures of different densities; using different density or granularity of solid particles to set different speeds in the liquid, some decanting centrifuges can also classify solid particles by density or particle size.
The fault monitoring and diagnosis of the operating state of the medical centrifuge system is to collect the signals of vibration, balance, temperature and pressure of certain sensitive parts of the equipment, and to identify the medical parameters by means of signal processing and extracting characteristic parameters. Machine working status. The basic principle is that during the operation of a medical centrifuge, its components will emit signals of their respective characteristics, which vary with the damage, wear and the voltage, current, temperature, etc. of the individual components. According to the characteristic parameters that characterize the specific faults, the artificial neural network monitoring and diagnosis system is used to diagnose and predict the medical centrifuge faults.
The fault intelligent monitoring and diagnosis system mainly includes two processes of monitoring and diagnosis. Each of these processes includes two parts: preprocessing and feature signal extraction. Quickly and efficiently extracting features that reflect equipment failure information is the key to fault diagnosis. The data obtained from the diagnosed object is treated as a set of time series. By segmenting the time series, the input data is mapped into points in the sample space, including data such as the type, extent and location of the fault. First, the input data mapped to the sample space is preprocessed, and another type of failure mode is obtained by deleting the useless information in the original data, and the sample space is mapped into the data space. On the basis of the data space, the invariant characteristics in the data are extracted to form a constant fault mode space. After extracting the invariant characteristics of the fault mode, according to the needs of the diagnosis and the characteristics of the problem, the selected mode feature vector is quantized and compressed, and useful features are selected for fault diagnosis.
Signal generation and propagation can be considered that the structure of the medical centrifuge system is a constant linear system, then the system vibration can be expressed as:
M[]{x}C[]{x}K[]{x}={Q}
M [] is the mass matrix of the system, {X} is the acceleration vector of the response, C [] is the damping matrix of the system, {x} is the velocity vector, [KI is the system stiffness matrix, and {x} is the displacement vector of the vibration. Q [] is the excitation force vector for generating the vibration of the medical centrifuge system.
The excitation sources of the vibration of the medical centrifuge system mainly include: motor drive system, machining error of the basket and the shaft, bearings and brackets, unbalanced assembly, water in the cavity formed by cracks in the test tube, and excessive temperature rise. The rotating shaft is seriously tilted and vibrated during high-speed rotation. When the vibration frequency exceeds the limit value, it will cause resonance of the entire system of the centrifuge, resulting in serious consequences.
The fault monitoring and diagnosis of the operating state of the medical centrifuge system is to collect the signals of vibration, balance, temperature and pressure of certain sensitive parts of the equipment, and to identify the medical parameters by means of signal processing and extracting characteristic parameters. Machine working status. The basic principle is that during the operation of a medical centrifuge, its components will emit signals of their respective characteristics, which vary with the damage, wear and the voltage, current, temperature, etc. of the individual components. According to the characteristic parameters that characterize the specific faults, the artificial neural network monitoring and diagnosis system is used to diagnose and predict the medical centrifuge faults.
The fault intelligent monitoring and diagnosis system mainly includes two processes of monitoring and diagnosis. Each of these processes includes two parts: preprocessing and feature signal extraction. Quickly and efficiently extracting features that reflect equipment failure information is the key to fault diagnosis. The data obtained from the diagnosed object is treated as a set of time series. By segmenting the time series, the input data is mapped into points in the sample space, including data such as the type, extent and location of the fault. First, the input data mapped to the sample space is preprocessed, and another type of failure mode is obtained by deleting the useless information in the original data, and the sample space is mapped into the data space. On the basis of the data space, the invariant characteristics in the data are extracted to form a constant fault mode space. After extracting the invariant characteristics of the fault mode, according to the needs of the diagnosis and the characteristics of the problem, the selected mode feature vector is quantized and compressed, and useful features are selected for fault diagnosis.
Signal generation and propagation can be considered that the structure of the medical centrifuge system is a constant linear system, then the system vibration can be expressed as:
M[]{x}C[]{x}K[]{x}={Q}
M [] is the mass matrix of the system, {X} is the acceleration vector of the response, C [] is the damping matrix of the system, {x} is the velocity vector, [KI is the system stiffness matrix, and {x} is the displacement vector of the vibration. Q [] is the excitation force vector for generating the vibration of the medical centrifuge system.
The excitation sources of the vibration of the medical centrifuge system mainly include: motor drive system, machining error of the basket and the shaft, bearings and brackets, unbalanced assembly, water in the cavity formed by cracks in the test tube, and excessive temperature rise. The rotating shaft is seriously tilted and vibrated during high-speed rotation. When the vibration frequency exceeds the limit value, it will cause resonance of the entire system of the centrifuge, resulting in serious consequences.
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