Intelligent identification of cable tension with damper based on deep learning

In order to address the challenges posed by the complexity and imprecision inherent in assessing cable tension with a damper in practical engineering, an intelligent identification method of the cable tension with damper based on IWPA-LKCNN-LSTM is proposed. The dynamic response test of the cable with a damper in practical engineering is carried out. Based on the data obtained from the test, a deep learning model that can intelligently identify the cable tension with a damper is developed. The model takes the cable tension, length, line density, frequency, and order as the feature inputs. First, the hyperparameters in the LSTM neural network are adaptively optimized by using the IWPA. Then LKCNN-LSTM is used for training. The intelligent recognition of the cable tension with a damper is realized. The average error between the recognized cable tension value on the test set and the actual cable tension value is a mere 2.024%, the mean square error value is only 0.0994%, the coefficient of determination is 0.9806, and the cable tension error is less than 5%. In conclusion, a comparison is made with the formula of cable tension calculation and other machine learning algorithms. The results show that this method can realize the intelligent and accurate recognition of the cable tension with a damper, signifying a broad spectrum of potential applications.