Online monitoring of cutting conditions is essential in intelligent manufacturing, and vibrations are one of the most effective signals in monitoring machining conditions. Generally, traditional wired accelerometers should be installed on a motionless or stable platform, such as a tool holder or lathe bed, to sense vibrations. Such installation methods would cause the signals to suffer more serious noise interferences and a low signal-to-noise ratio, resulting in less sensitivity to valuable information. Therefore, this study developed a novel three-axis wireless on-rotor sensing (ORS) system for monitoring the turning process. The Micro Electromechanical System (MEMS) accelerometer sensor node can be mounted on a rotating workpiece or spindle rotor and is more sensitive in detecting the vibrations of the entire rotor system without any modification of the lathe system and interference in the cutting procedure. The processor, data acquisition, and Bluetooth Low Energy (BLE) 5.0+ modules were developed and debugged to cooperate with a piezoelectric triaxial accelerometer, with a vibration amplitude not larger than ± 16 g. A series of turning tests were conducted and the results were compared with those from the commercial wired accelerometers, which proved that the ORS system can measure the vibration signal of the rotor system more effectively and sensitively than wired accelerometers, thus demonstrating the accurate monitoring of machining parameters.

Electric linear or rotary actuators are the ultimate power-dense execution units in modern industrial and transportation systems, yet their dependability is directly governed by the health of the driving electric motor. To guarantee fail-safe operation of the electromechanical actuator chain, condition monitoring and fault diagnosis of the embedded motor have become indispensable. The motor fault diagnosis process can be comprehensively summarized into four key steps: signal acquisition, feature extraction, condition monitoring, and fault identification. Based on the data obtained by signal acquisition, machine learning methods can be effectively integrated into the latter three steps. Feature extraction techniques primarily revolve around autoencoders. In terms of condition monitoring technology, in-depth research has been conducted on image recognition, including the identification of two-dimensional and three-dimensional images. In terms of fault identification, various machine learning methods have been applied, such as convolutional neural networks, autoencoders, transfer learning, long short-term memory networks, and support vector machines. Finally, the potential application of the Large Language Model in motor fault diagnosis was explored.

The chatter that occurs during the turning operation, especially when cutting a slender and flexible shaft, determines the surface quality of the workpiece and the stability of the machining system. However, when building a dynamic model of a slender workpiece with a chuck and tailstock, it is generally regarded as a cantilever or simply supported beam, without consideration of the axial force and supported stiffness effect. In this work, a dynamic model for thin and flexible workpieces with different clamping boundary conditions was first built. Then, a finite element analysis (FEA) was used to study the influence of the axial force and supporting stiffness on the mode frequencies of the workpiece. A further analysis found that the relationship between support stiffness, axial force, and the dynamic response of the workpiece is nonlinear and far more complex than that of the simply supported beam model. The clamping force directly influenced the magnitude of the vibration response with the decrease of shaft stiffness during the turning process. These results were verified experimentally by measuring the vibrational response of slender shafts with different clamping modes using an on-rotor sensing (ORS) system. It proved that the proposed model shows advantages for the identification of dynamic vibration and quality control when machining slender workpieces.

In marine electric propulsion systems (MEPS) driven by variable-frequency drives, motor current signals often exhibit complex modulation components, ambiguous spectra, and severe noise interference, rendering it challenging to extract fault-related modulation components. To address this issue, we propose a zero-crossing tacholess order tracking method based on motor current signals. This method utilizes zero-crossing estimation of the instantaneous frequency to perform angular resampling of stator current signals and demodulates the envelope spectrum to extract fault characteristic spectra, enabling the diagnosis of mechanical faults in MEPS. Given the synchronization of the synchronous motor speed with the inverter fundamental frequency, this method estimates instantaneous frequencies in the time domain without requiring integration or time–frequency representation, which is simple and computationally efficient. Data validation on a small-scale marine electric propulsion test platform demonstrates that the proposed method exhibits good robustness under variable-speed conditions and effectively detects imbalance faults caused by propeller breakages and gear faults resulting from bevel gear tooth defects. Therefore, the proposed method can be applied to diagnose faults in downstream mechanical equipment driven by motors.