Online Fault Diagnosis of Industrial Robot Using IoRT and Hybrid Deep Learning Techniques: An Experimental Approach

The Internet of Robotic Things (IoRT) is growing rapidly with new applications. Co-operatory robotics enables the sharing of information, autonomy, and fail-safe interaction with environment, humans, and other robots. They can also self-maintain, self-aware, and self-heal. To provide reliable and robust online monitoring of the industrial manipulator joint status, this article proposes a new IoRT architecture based on transfer learning (TL) techniques to detect manipulator fault. Robotic manipulator joint status are detected with high accuracy using a hybrid 1-D multichannel convolutional neural network (1D-MCNN), including matrix kernels and recurrent neural network (MCNN-RNN) technique. Moreover, a timestamp mapping method addresses the challenges associated with inconsistencies in sensor data timestamps. Existing data-driven methods struggle with the diverse operating conditions of industrial robots, where load and speed constantly fluctuate. To address this limitation, we propose a novel TL-based MCNN-RNN approach for joint fault diagnosis under varying work conditions. This method leverages the adaptability of TL while incorporating the inherent relations between different failure modes, enhancing the TL process. To demonstrate the performance of the suggested IoRT topology, various experimental scenarios are performed with data acquisition on six degree-of-freedom (DOF) UR16e (universal robot) manipulator. Based on the results, the proposed IoRT architecture can effectively visualize the joint fault status of the manipulator. As a result, TL architecture combined with MCNN-RNN provides an excellent accuracy of 99.03%in detecting faults on manipulator joints, which is significantly higher than traditional convolutional neural network (CNN), deep belief network (DBN), domain adversarial neural network (DANN), and conditional domain-adversarial network (CDAN).

I. Introduction

The Internet of Things (IoT) and Internet of Robotic Things (IoRT) are the products of the recent rapid evolution of both the Internet and “things.” IoT robots can securely interact with people, the environment, and other objects; they can learn independently, communicate with one another and fix themselves if they break; and they can fail operationally if they malfunction. Individual, collaborative, and collective intelligence of robotic objects, as well as information from the infrastructure and operational context, may be used by IoRT applications to plan, execute, and complete tasks under variable environmental circumstances and uncertainties. Perception, localization, communication, cognition, computation, connection, propulsion, and integration are all growing in significance as federated IoRT and digital platforms continue to engage with the environment in real time. Product quality, system productivity, and sustainability can all be enhanced while costs are reduced through the use of Internet-connected, sensor monitoring, and computationally intelligent machinery in smart manufacturing [4]. The automotive, electronics, consumer packaged goods, and aerospace manufacturing industries are just a few examples of sectors that can benefit from the employment of industrial robots in smart manufacturing systems [5]. A robot system consists of the robot itself, its end-effectors, and any additional hardware or sensors required to complete the task at hand [6]. It is not uncommon for complex robotic systems to experience problems like sudden shutdowns and a drop in quality. It has been estimated that the loss to automakers due to unscheduled downtime of industrial robots is over 20000 per minute [7]. Consequently, keeping an eye on the well-being of factory robots is crucial.