Traditional bearing fault diagnosis mainly based on periodic feature extraction from bearing fault pulses. However, the diagnostic accuracy was limited due to the absence of rotation speed information. Firstly, through envelope analysis and order tracking to extract roller passing frequency (RPF) and fault order, this paper verifies the performance of vibration and acoustic emission signals (AE) on diagnosis of bearing fault in low-speed. Secondly, the advantages of fault source localization of AE are also verified and analyzed. In order to accurately extract the pulse generated by the fault source, a method for AE event extraction using short time autocorrelation function is proposed, which is verified to be efficient and reliable under different working conditions of low-speed bearings.

The flexibility of the joint drive system of an industrial robot can cause vibration at the end part, which can lead to motion errors. A method to predict the vibration during the motion of the robot arm is proposed considering the robot joint flexibility. The method combines the internal transfer function of the drive system and the identification of parameters under external excitation. Firstly, the dynamics of the robot joint system are modeled by a double inertia elastic system. The joint system transfer function from the electromagnetic torque to the arm vibration is obtained according to the dynamics model. To solve the unknown parameters in the transfer function, a vibration dynamics model of the joint arm under the external forces on the arm is developed. According to this model, the equivalent stiffness, damping and load inertia of the joint can be obtained by the direct parametric method. Then, the vibration spectrum of the robot arm is derived from the motor electromagnetic torque and joint dynamics models were used to predict the vibration spectrum of the robot arm. The experiments were conducted on a single-joint robot testbed, and on an articulated industrial robot. In both experiments, the key parameters in the system were determined by impact experiments. Then, the vibration signal of the arm during the robot motion was obtained by electromagnetic torque prediction. The predicted vibration signals are analyzed in comparison with the actual vibration signals. The experimental results both show the validity of the vibration prediction.

Important state parameters, such as torque and angle obtained from the servo control and drive system, can reflect the operating condition of the equipment. However, there are two problems with the information obtained through the network from the control and drive system: the low sampling rate, which does not meet the sampling theorem and the nonuniformity of the sampling points. By combing equivalent sampling and nonuniform signal reconstruction theory, this paper proposes a reconstruction method for signal obtained from servo system in periodic reciprocating motion. Equivalent sampling combines the low rate and nonuniform samples from multiple periods into one single period, so that the equivalent sampling rate is far increased. Then, the nonuniform samples with high density are further resampled to meet the reconstruction conditions. This step can avoid the amplitude error in the reconstructed signal and increase the possibility of successful reconstruction. Finally, the reconstruction formula derived from basis theory is applied to recover the signal. This method has been successfully verified by the simulation signal of the robot swing process and the actual current signal collected on the robot arm testbed.

Rotation vector (RV) reducers have been widely used as an important part of robot joints, and the health monitoring of RV reducers needs to be studied urgently. Traditional vibration analysis faces challenges due to the vibration spectral complexity introduced by time-varying transmission path and interferences of other components in reducers. The rotary encoder contains rich diagnostic information, which provides a new way for fault diagnosis of rotating machinery. This paper presents a feasible strategy for health assessment of RV reducers using encoder signal. In this algorithm, the angle sequence is firstly converted into instantaneous angular speed (IAS) by central difference method (CDM). Then, considering the amplitude modulation characteristics of the fault impact, a scheme for extracting shock transients based on envelope demodulation is determined, and a fault crest proportion index defined on the envelope power spectrum (Epfc) is designed. The optimal filtering band of IAS is determined through the proposed index and band-pass filtering is performed by utilizing Morlet wavelet. Furthermore, band-pass filtering is further implemented on the envelope of the filtered IAS to reduce noise. Finally, transient impulses are extracted by synchronous averaging (SA) to remove the interferences of random shocks. In this step, cross-correlation is employed to correct the phase deviation of the IAS signal and improve the effect of SA. The effectiveness of the proposed method is verified by the fault experiment of RV reducer.

The difficulty of adding external excitation and the asynchronous data collection from the industrial robot operation limited the online parameter identification of industrial robots. In this regard, this study proposes an identification method that only uses the amplitude of the frequency response function (FRF) of the system to identify robot joint torsional stiffness and dynamic parameters. The error criterion function shows that this method is feasible and comparable to applying the complete frequency response for identification. The Levenberg–Marquardt (L-M) algorithm is used to find the global optimal value of the error criterion function. In addition, an operational excitation method is proposed to excite the system. The speed profile is set as a triangle wave to excite the system using rectangular wave electromagnetic torques. The simulation results show that using the amplitude of the FRF to identify parameters applies to asynchronous data. The experiments on a single-degree-of-freedom articulated arm test bench show that the motion excitation method is effective, and both stiffness and inertia are identifiable.

In the identification of industrial robot joint system, in order to obtain the ideal frequency response of the system, additional excitation needs to be applied. The addition of excitation signals such as chirp signals and pseudo-random binary signals will make the instantaneous angular speed (IAS) obtained by the motor encoder present non-stationary characteristics. In order to solve this problem, this paper proposes a method for system identification by IAS extraction under non-stationary conditions. Interferences such as electrical perturbation, quantization error, background noise generally reduce the accuracy of extracted IAS. Firstly, the influence of electrical perturbation on the accuracy of angle calculation is eliminated by utilizing the characteristics of the two-phase encoder signals. Secondly, considering the time-varying characteristics of the rotational speed in the identification process, a non-uniform local polynomial differentiator (NULPD) is proposed to optimize the estimation of IAS and reduce the influence of background noise and sampling error based on local polynomial differentiator (LPD). The IAS estimation under the condition of variable speed is simulated to verify the performance of NULPD. According to the transmission characteristics of the robot joint system, a two-mass dynamic model is established, and the transfer function between the torque and motor speed is obtained. The torque is calculated from the three-phase current signals, and the rotational speed is obtained by the encoder signal. The frequency sweep experiment is carried out on the single-degree-of-freedom articulated arm test bench, where the stiffness and inertias of the system are identified within a certain error range by the proposed method.