Classification Methods for Motor Vibration in Predictive Maintenance

Reducing costs is an important part in todays business. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.

Design and Construction of Additional Security Device for House Door Using Arduino Uno Based on Short Message Service

Security is a very important aspect of a house. The level of protection and comfort of the house certainly cannot be separated from how good the security system of the house is. The door is used for the main entrance and exit of the house. When the owner of the house is inside, the door can be locked from the inside with the addition of a slop lock on the door. However, if the owner of the house leaves, the door is only locked using a regular key without any additional keys on the inside of the door. So that the door of the house can be opened only by "lockpick" by an irresponsible person. Such problems can be prevented by installing additional security devices on the door of the house that can be controlled to lock the inside of the door and can provide early warning to homeowners through SMS (Short Message Service) notifications on cell phones. The components used in this system are Arduino Uno microcontroller, Servo Motor, Vibration Sensor (SW-420), Buzzer, Magnetic Switch MC-38, and GSM SIM800L V2. The prototype worked well and reach the research’s objective.

Motor Fault Diagnosis Algorithm Based on Wavelet and Attention Mechanism

In order to improve the maintenance efficiency of the motor and realize the real-time fault diagnosis function of the motor, a motor fault diagnosis algorithm based on wavelet and attention mechanism is proposed. Firstly, the motor vibration signal is decomposed by wavelet transform, and the high-frequency signal is denoised to improve the signal-to-noise ratio. Secondly, the frequency band and time dimension after wavelet decomposition are taken as input data, the convolution neural network is used to fuse the frequency band features of data, and the bidirectional gated loop unit is used to fuse the time series features. Then, the attention mechanism is used to adaptively integrate the features of different time points. Finally, motor fault diagnosis and prediction are realized by classifier recognition. Experimental results show that, compared with the existing deep learning fault diagnosis model, this method has higher diagnosis accuracy and can accurately diagnose the running state of the motor.

The Influence of the Motor Traction Vibration on Fatigue Life of the Bogie Frame of the Metro Vehicle

During the service life of metro vehicles, the cracks frequently appear at the root of motor seats. This indicates the previous antifatigue designs are unable to cover the actual operating environment. Some individual loads, such as motor vibration, have been ignored or wrongly understood, which leads to the occurrence of local insufficient fatigue life of the frame. To illustrate the influence of the motor vibration on the fatigue life of the bogie frame, a metro vehicle was taken as an example: first, the precise finite element model of the frame was established, and its correctness was verified; then, the vibration characteristics of the frame were analyzed by sweep frequency calculation; and finally, considering the vibration acceleration signal of the motor measured on a metro line as the excitation, the influence of the random vibration on the fatigue life of the frame under traction and idle running conditions was compared and analyzed by solving the power spectral density of the dynamic stress response at the weak fatigue nodes of the structure. The results show that the energy of the vibration in the frame is mainly concentrated in modes 6 and 7, which are excited by the motor transverse and vertical vibration, respectively, and contribute a lot to the fatigue damage of the frame; the fatigue life of the vital positions of the frame under the traction condition significantly reduces compared to the idle running condition; and the contribution of the low-amplitude vibration above 300 Hz to the fatigue damage can be ignored. At last, the importance of the influence of the motor traction vibration on the fatigue life should be fully considered in the metro vehicle design was proposed.