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Fault Detection via power consumption monitoring of industrial robots is a substantial problem considered in this work, in which the healthy measurements of power consumption and encoders data for a pre-specified task are employed as a reference for comparison to diagnose the potential failures or excessive degradation in the robot joints. Since mo...
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... industrial robot of 6 degrees of freedom (DOFs) ABB IRB is employed to demonstrate the proposed modeling of energy consumption based on power rate measurements. The stroke, reach, working range, load, and the robot's frame assignments are shown in Fig. 7. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI ...
Context 2
... industrial robot of 6 degrees of freedom (DOFs) ABB IRB is employed to demonstrate the proposed modeling of energy consumption based on power rate measurements. The stroke, reach, working range, load, and the robot's frame assignments are shown in Fig. 7. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI ...
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Three-wheeled omnidirectional mobile robots (TOMRs) are widely used to accomplish precise transportation tasks in narrow environments owing to their stability, flexible operation, and heavy loads. However, these robots are susceptible to slippage. For wheeled robots, almost all faults and slippage will directly affect the power consumption. Thus, using the energy consumption model data and encoder data in the healthy condition as a reference to diagnose robot slippage and other system faults is the main issue considered in this paper. We constructed an energy model for the TOMR and analyzed the factors that affect the power consumption in detail, such as the position of the gravity center. The study primarily focuses on the characteristic relationship between power consumption and speed when the robot experiences slippage or common faults, including control system faults. Finally, we present the use of a table-based artificial neural network (ANN) to indicate the type of fault by comparing the modeled data with the measured data. The experiments proved that the method is accurate and effective for diagnosing faults in TOMRs.
Industrial robots are prone to failure due to harsh working environments, which affects movement accuracy. The fault diagnosis of industrial robots has become an indispensable part of robot collaborative maintenance in intelligent manufacturing. Most existing diagnostic methods only use a single data source, and the diagnostic accuracy will be affected due to signal acquisition errors and noise interference. This paper proposes a multi-source data fusion and channel attention convolutional neural network (MD-CA-CNN) for fault diagnosis of multi-joint industrial robots. The network takes the time domain data and time-frequency domain data of the vibration signal, torque signal, and current signal of the six joints of the robot as input. Then, we realize the diagnosis of the faults by using a Softmax Classifier layer after the two parts of feature extraction and feature fusion. In addition, a channel attention mechanism is developed. It acts on the two parts of feature extraction and feature fusion, respectively. It assigns weights to different source data and weights to time-domain and time-frequency domain features. Finally, we established a test bench to compare the proposed method with the deep learning algorithm that only uses multi-data source fusion, the deep learning algorithm that only uses a single data source, and the commonly used machine learning algorithm. The results show that the MD-CA-CNN model proposed in this paper has the highest accuracy and stability, reaching 95.8% ± 0.39%, which verifies the method’s effectiveness.
So far, existing publications related to energy simulation of production systems predominantly focus on application or simulation aspects, with a particular emphasis on discrete event simulation (DES) of process chains. However, the modeling of energy aspects, even beyond the typical DES area, is crucial for a successful simulation project in terms of analyzing and optimizing energy-saving potentials within the industry. Therefore, this paper focuses on major techniques used to model the energy behavior of production systems across all levels of production systems, from individual components to a network of factories. Through a structured literature review, we identified the primary modeling techniques and typical characteristics for energy modeling and structured them in the form of a taxonomy.
In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies.
Motor bearing is the key vulnerable part of the servo motor in an industrial robot, which is always arranged at the joint that is the main load area. In the movement process of the robot, motor bearing bears a great impact due to the frequent movement of joints, which is easily damaged. The fault characteristic information of a bearing in these complex conditions shows strong non-stationary features. Early non-stationary fault signals are often weak and submerged in background noise. The non-stationary signal processing method using computed order analysis and the weak signal enhancement method using adaptive stochastic resonance both show good performances for the above problems. Inspired by these, a hybrid diagnosis strategy for motor bearing under these speed conditions is proposed. Firstly, the non-stationary fault signals of the motor bearing are transformed into stationary angular signals via computed order analysis. Then, the fault modes are identified via resonance demodulation and variational mode decomposition in the order spectrum. Finally, adaptive stochastic resonance is used to extract the fault features reflecting the bearing operation state. Two types of typical speed conditions are considered, which is representative at the joint. Numerical simulation analysis and experiments verify the effectiveness of the diagnosis method.
