Multisensor information fusion, when applied to fault diagnosis, the time-space scope, and the quantity of information are expanded compared to what could be acquired by a single sensor, so the diagnostic object can be described more comprehensively. This paper presents a methodology of fault diagnosis in rotating machinery using multisensor information fusion that all the features are calculated using vibration data in time domain to constitute fusional vector and the support vector machine (SVM) is used for classification. The effectiveness of the presented methodology is tested by three case studies: diagnostic of faulty gear, rolling bearing, and identification of rotor crack. For each case study, the sensibilities of the features are analyzed. The results indicate that the peak factor is the most sensitive feature in the twelve time-domain features for identifying gear defect, and the mean, amplitude square, root mean square, root amplitude, and standard deviation are all sensitive for identifying gear, rolling bearing, and rotor crack defect comparatively. 1. Introduction Typical rotating machinery systems such as water turbine, steam turbine, wind turbine, and rotary kiln are critical core equipment support of the important industries of the national economy [1, 2]. The safety, reliability, efficiency, and performance of rotating machinery are major concerns in industry, so, the task of condition monitoring and fault diagnosis of rotating machinery is significant [3]. The common mechanical defects of rotating machinery are divided into three categories: rotor body defects, such as unbalance, misalignment, rubbing, and rotor crack; rotor support-bearing defects, such as inner race, outer race or ball defect of rolling bearing, and oil whirl or oil whip of sliding bearing; transmission gear defects, such as chipped tooth defect or missing tooth defect. In-process monitoring and diagnostics of rotating machinery require reasoning about defect and process states from sensor readings. Often the relationship between the sensor readings and the process states is complex and nondeterministic. For a complex system, a single sensor is incapable of collecting enough data for accurate condition monitoring and fault diagnosis. Multiple sensors are needed in order to do a better job. When multiple sensors are used, data collected from different sensors may contain different partial information about the same machine condition. The diagnostic object can be described more comprehensively [4–6]. Compared with single sensor, the time-space scope and the quantity
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