###### Abstract
In this study, the diagnosis procedures were mainly defined by classifying them into five stages: from data gathering to health maintenance, as shown in Figure 1.  
In this study, the suggested approach is used to analyze sensor data obtained from the plant process and apply it to the condition diagnosis of the process equipment. The plant process was generally equipped with several pieces of rotatory equipment, such as compressors, pumps, motors, and turbines, as well as heat exchangers, separators, synthesizers, valves, piping, and pressure vessels. In particular, the rotary device had potential failure modes, such as misalignment between components, overheating, plugging, liquid leakage, abrasion, wear-out, and corrosion [12; 13]. The technology to monitor the operation conditions and identify the fault status by attaching sensors to each piece of equipment has a long history. It is a very common maintenance method to accumulate  
Figure 1: Overall procedure of diagnosis method. the time series signals, feature values, and images obtained from the sensors in the data acquisition system and analyze each signal to determine the fault indications [14; 15]. In particular, one method that is widely used for determining a possibility of failure is when the failure indication value--extracted from the measured signal data--exceeds the upper and lower limit values. In other words, an upper or a lower limit (threshold) was set for each signal, and the equipment is checked when the measured signal is out of the threshold value, and the possibility of failure is diagnosed based on the failure history. Rotatory machines are some of the most typically monitored pieces of equipment that are subject to various faults (e.g., vibration, shaft alignment, bearing breakage, and overheating) [16]. Failure detection of the rotatory machine is possible by analyzing time series data of temperature, acceleration, displacement, stress, lubrication status, etc. Generally, a signal processing method, such as the wavelet transform or the Hilbert-Huang transform, could detect fault signals using the time series signal analysis method [17; 18]. In order to apply the signal processing methods, the data collection frequency should be 10 kHz or more, but the storage capacity of the sensor data acquisition device cannot afford the amount of data gathered at the frequency of KHz, in practice. For this reason, in this study, sensor data of the onshore plate were collected at a frequency of less than 1/min, and applied in order to analyze data patterns using statistical features of the data. Data patterns and statistical processing of error signals are suitable for overcoming the limitations of time series data and performing error diagnostics [18]. In this study, the diagnostic methodology applied was for a main air blower (MAB) unit operated in the fluid catalyst cracking (FCC) process. The main air blower is a major piece of equipment utilized for air circulation of the FCC process in petrochemical plants and is one of the main equipment that can cause significant losses throughout the process in the event of a failure. Stable operation of the air blower greatly influences the safe production of the entire FCC process [19; 20]. Operation data (sensor data) obtained for MAB equipment in the FCC process were collected and applied to the diagnosis method. The collected sensor signal was labeled based on the acquired sensor data and the failure history for the MAB equipment, and the normal and the failure condition were patterned and classified, respectively. The statistical characteristics of the time series signals were used to overcome the limits of the low-period measured signals. Classified data were derived from the statistical features of the data and used as representative values for each operating state. Once the data patterns of normal and failure conditions were defined, we applied dimensional reduction methods to simplify and establish the status criteria.