Mechanical Acoustic Signal Assisted Translational Model for Industrial HumanMachine Interaction Humanmachine interaction (HMI) is a popular issue in industrial intelligent manufacturing research, and it is of great significance to realize mechanical anthropomorphic operation. This paper proposes a novel and universal translational model based on mechanical acoustic signals with visual information for expert operation imitation in mechanical operation environment. Through reasonable state characterization and feature extraction, the translational model is approximated as a hidden Markov model (HMM) and it is also designed for three challenges of industrial HMI: (i) ExpectationMaximization (EM) algorithm is first adopted to achieve signal fusion to relieve the interference of external noise in the propagation of mechanical acoustic signal. (ii) Adaptive clustering method is then designed to realize online reorganization of state points to obtain the same modelling effect as offline to overcome the bottleneck of modelling accuracy and online rapid application. (iii) Output prediction based on hneighborhood is finally proposed to deal with situations of less data amount or data distortion. For above three points, this paper gives a complete design framework with key formulas and processing methods. Through the verification of typical industrial HMI application of rivet plugging, the proposed translational model can achieve RMSE of 0.1231 and at least 14.3% performance improvement compared with traditional methods.

Industrial Robot Rotate Vector Reducer Fault Detection Based on Hidden Markov Models Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the longterm maintenance of highprecision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and twostage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal preprocessings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults.