Abstract
In this paper, a novel and more effective fault diagnosis approach for wind turbine planetary gearbox (PGB) is proposed. In order to better detect the faults in the early stage of the faults of the wind turbine PGB, the corresponding maintenance measures can be carried out to prevent the faults from becoming more serious, so as to seriously affect the normal operation of the fan gearbox. The gear with lighter fault degree is used to simulate the early fault signal. Compared with the fault which has seriously affected the normal working condition, the fault characteristics of the early fault signal are more difficult to detect. So in this design, deep belief network (DBN) optimized by quantum particle swarm optimization (QPSO) algorithm is used to extract deeper and more identifiable features of slight fault signal. After optimization by QPSO algorithm, DBN can get a most suitable structure according to the actual working signal of fan gearbox. Then these extracted features are input into the least squares support vector machine (LSSVM) optimized by QPSO for fault diagnosis test. At the same time, the wireless sensor nodes using self-energy in vibration state are optimized. By using microcontroller unit (MCU) MSP430F149 and nRF24L01 radio frequency (RF) chip with lower energy consumption, the normal dormant state can be maintained, the power requirement of transmission mode can be met, the stability of the whole node can be improved, and the phenomenon of energy shortage caused by short-term fluctuation can be prevented. The comparative experiments in this paper show that this method has good effect on the fault diagnosis of wind turbine PGB.
Highlights
Wind power generation made great process in recent years [1], the maintenance costs of wind power generation system are correspondingly higher than traditional ones [2], because wind turbines are hard to access
In this paper, we propose an early fault diagnosis method for wind turbine planetary gearbox (PGB) based on surrounding energy harvesting wireless sensor, deep belief network (DBN) and least squares support vector machine (LSSVM)
The vibration condition of wind turbine PGB is the signal source collected by wireless sensor, and the energy source to ensure its normal operation
Summary
Wind power generation made great process in recent years [1], the maintenance costs of wind power generation system are correspondingly higher than traditional ones [2], because wind turbines are hard to access. L. Lu et al.: Wind Turbine PGB Fault Diagnosis Based on Self-Powered Wireless Sensor and Deep Learning Approach time-frequency analysis [12], sparse representation [13] cannot extract effective and differentiated features from intricate original vibration signals, nor can reliable fault diagnosis be achieved in the follow-up. We can get the hidden features in a deeper level, so as to discover more deep-seated laws In this design, DBN method is used to extract fault features from the original vibration signals of the wind turbine PGB. The method of combining DBN and LSSVM is used to diagnose the PGB of wind turbine Sensors such as accelerometers are commonly employed to collect vibration signals, this way of adopting sensors is similar in current-based [18]–[20] and acoustic emissionbased [21] condition monitoring.
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