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By connecting the symmetrical spiral inductive sensor to the oil return pipeline of the lubricating oil, a full flow online detection of the size, quantity and other parameters of the tiny metal debris particles in the lubricating oil solution is realised. Inductive sensors are used to excite symmetrical double helix coils, and alternating electromagnetic fields are generated. Changes in induced electromotive force are detected by the centre induction coil. In order to improve the detection accuracy of tiny metal debris particles, the degree of anti-jamming of the excitation signal, and the transmission distance, a method of stimulating using sinusoidal AC steady current signals was designed. The sinusoidal signal generator is designed based on FPGA, and the sinusoidal AC steady current excitation signal is realised through differential amplification, low-pass filtering, and AC steady current amplification, and experimental verification is performed to achieve better excitation and detection results.

Wear is one of the common faults in the mechanical transmission system. Detection of wear particles in lubricating oil can detect and evaluate the wear degree of mechanical equipment. The electromagnetic sensor for metal wear particle detection can be connected in series with the oil circuit, which has the advantages of full liquid flow, simple structure, distinguishing metal properties, and not being influenced by bubbles and vibration. In view of the shortcomings of the existing electromagnetic sensor for wear particle detection, the detection principle and the induction electromotive force model were studied, and the magnetic field change of the metal particles through three coil differential solenoids was analysed. The optimisation design method was proposed here, which was about the related parameters, such as the wire diameter, the coil turns, the coil widths and so on. The experimental analysis of the sensor was carried out based on detection hardware system that could provide the phase-locked amplification function. The 10-mm inner diameter sensor could detect the minimum 100-μm ferromagnetic metal abrasive particles after optimisation. The developed sensor could provide data information for the abrasive particle detection, fault diagnosis and early warning of wear degree of mechanical equipment.

Online oil monitoring technique is now an important development means to monitor wear condition and diagnose wear fault real time. Inductive wear debris monitoring is the research hotspot in the current online oil monitoring for its unique characteristics. Based on the current domestic and international online inductive wear debris monitoring, the relevant comments to the online inductive wear debris monitoring technology were given. Then, the positive and inverse problems of numerical analysis of inductive wear debris monitoring are analysed. Based on the above, the difficulties and shortages of online inductive wear debris monitoring are studied and the development trend and research focus of online inductive wear debris monitoring are discussion to provide for the oil online technology research.

In order to improve the operation reliability of high-grade computer numerical control (CNC) machine tools and reduce the maintenance time of failures, the remote state monitoring and intelligent maintenance system for CNC machine tools was proposed. Through collecting the basic information of equipment, maintenance information, and the information of the vibration, temperature, main current and power of the machine tool based on soft-bus technology, the system analysed the real-time state data, got the reliability parameters, extracted the fault features and established the maintenance knowledge base. Combining the dynamic monitoring data and static data, the system evaluated the key parts of machine tools. By the fault knowledge base, the system analysed the reason for failure and recommended the maintenance method. With the function modules, the system can improve the intelligent maintenance level of CNC machine tools and improve the using efficiency of machine tools.

Gear and its transmission are widely used in different transmission systems, and its complicated and changeable condition brings a series of problems to the fault feature extraction and diagnosis. In recent years, deep learning techniques have been gradually applied to feature extraction and pattern recognition, and the features of feature extraction and fault diagnosis in complex working environments have shown certain advantages. This study is based on stacked autoencoder under deep learning model, and improve training network performance by modified activation function. Through the network training before and after the experiment done, and to extract the fault feature data comparison in testing, improving network after activation function to extract fault features showed a greater advantage, can be a very good application in practical fault feature extraction.

Lubrication is one of the critical factors for the normal operation of wind turbine's gearbox, which carries abundant and various wear debris. Accordingly, these debris reflects the running state of the equipment and forecasts the early fault of wind turbine gearbox effectively. Thus, the authors can achieve predictive maintenance, even avoid the occurrence of safety accidents. In order to realise real-time online monitoring of lubrication, this article studied the principle of wear debris detection sensor, constructed approximate mathematical model of inductive wearing debris sensor, produced a prototype device, and carried out the monitoring test of metal wearing debris without oil. Experiment shown that the designed sensor can detect the signal of metal wear debris with the smallest diameter to 80μm, which can diagnose the early fault of the wind turbine.

In view of the stability and safe service of the transmission system of the wind turbine, the trend early warning method for the stability deterioration of the electromechanical system is proposed and the level of the predictive maintenance of the complex electromechanical system is improved. It includes: establishing the dynamic model of wind power transmission system, revealing the evolution law of the system stability deterioration, putting forward the signal processing method of the weak detection information, extracting the fault features decoupling with energy and constructing the early warning methods of the system stability deterioration trend. The research results can be applied to the safety monitoring and fault warning of the wind turbine. It is beneficial to ensure the safe and reliable operation of the equipment and realise the prediction maintenance, improve the operation and management level of the equipment, reduce the economic loss caused by the failure and promote the utilisation and development of green energy.