The acoustic method is sensitive to agglomerations in fluidized bed reactors; however, a single acoustic sensor is not sufficient for industrial-scale reactors with regard to size, environmental noise, and signal pollution. Therefore, a multiacoustic sensor-based monitoring approach is investigated in this study. Different agglomeration warning models are established on the basis of each separate sensor, and then information-fusion technology is introduced. In the modeling process, an early warning method for industrial applications is designed. First, singular value decomposition is introduced for feature extraction to simplify the signal preprocessing and reduce the number of preset parameters. Thereafter, as agglomeration samples are lacking in industrial data, support vector data description (SVDD), an unsupervised learning method, is employed to establish the agglomeration warning model according to samples under normal conditions. As the Boolean outputs of SVDD are not suitable for information fusion, the output probability estimation method for SVDD is studied. In the estimation method, additional sigmoid functions are incorporated into the SVDD models, and the maximum likelihood algorithm is combined with the expectation-maximization algorithm to optimize the parameters of the sigmoid functions. Finally, the Dempster-Shafer evidence theory is applied for information fusion. Experiments conducted on a pilot plant prove that the proposed early agglomeration detection method is more stable than the S statistics of acoustic signals. The multisensor-based warning method can provide better detection of agglomeration than the temperature-pressure method, and for industrial applications, it provides more reliable detection results than single-sensor systems.