The existing engineering signal processing methods are based on complete data acquisition, which do not consider the missing signal processing. However, in engineering practice, due to human factors and natural irresistible factors, the sensor may fail and result the lack of signal acquisition. To eliminate the negative influence of signal loss on engineering signal processing, a signal recovery method based on the variational Bayesian parallel factorization is proposed. Firstly, the collected vibration signal is constructed into a three-dimensional tensor by the parallel factor analysis theory. Meanwhile, combined with the Bayesian method, potential variables and super parameters are introduced to formulate Bayesian parallel factor probability graph model. Then, the posterior distribution of the factor matrix and the super parameters are derived by the variational Bayes algorithm. Therefore, the distribution prediction of the missing element can be further deduced. Finally, the proposed algorithm can better solve the problem of signal loss by analyzing the lower bound of the model and the selection of initialization parameters. Two evaluation indexes ( i. e. root mean square error and root relative squared error) are used to evaluate the performance of the algorithm. The simulation and experiment results show that with the increase of missing ratio, the variational Bayesian parallel factorization algorithm has smaller error than the traditional low rank tensor completion algorithm, which can more effectively restore the missing signal. The proposed method provides an effective way to solve the problem of signal missing caused by sensor failure in engineering signal processing.