To minimize the operation and maintenance costs of rotating machinery, damaging conditions should be detected in the early stages. In recent years, infrared (IR) imaging and acoustic-based condition monitoring methods have gained attention for this purpose. To solve the problems when one single type of data, either acrostic signals or IR images, cannot be individually used to assess the severity of faults in rotating machinery, in this paper we proposed an information fusion method to improve the utilization of multisource sensor systems. Fusing the extracted information from both sources is performed with the aid of a fuzzy inference system. The effectiveness of the proposed techniques is demonstrated through the data that has been captured by an inspection mobile robot for monitoring conveyor belt idlers at a mining site