# A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure
## 3 Approach  
This section describes an approach to identify the condition of plant equipment based on gathered sensor data. Figure 1 depicts an overview of the approach proposed in this study.  
As shown in Figure 1, the approach we proposed consisted of two parts. One part is the learning process needed to build up the learning model for identifying the state of the plant equipment based on the gathered data and the other part is the prediction process used to foresee the state of the equipment with the learned model. The following is a description of the symbols used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Authors** & **Subject** & **Approach** \\ \hline [PERSON] and [PERSON] (2007) & Unlike HMMs with fixed state transition probability, the HSMM technique considers the change of the state transition probability over time, suggesting a method to diagnose and predict the condition of equipment. & Hidden semi-Markov model \\ [PERSON] and [PERSON] (2007) [12] & They applied a partly-linearized neural network in order to diagnose the centrifugal pump failure. To this end, they extracted relevant features using a continuous wavelet transform technique. & Continuous Wavelet Transform, Partially-linearized neural network \\ [PERSON] and [PERSON] (2008) & They proposed a methodology to diagnose faults in sewer pipes by extracting organizational characteristics of pipe faults in CCTV images using SVM. & SVM, Radial Basis Network, Back-Propagation Neural network \\ [PERSON] and [PERSON] (2009) & They applied the SVM with the Genetic Algorithm (SVMG) method for diagnosing power transformation failures based on gas information gathered from the equipment. & Support Vector Machine with Genetic Algorithm \\ [PERSON] and & With the model combining linear regression with quadratic regression, & Regression \\ [PERSON] (2009) & they predicted the deterioration of a gas turbine engine. & Regression \\
[15] & They proposed the FDS (Fault Detection System) using a feature-based decision tree based on the data collected from the sensor of the equipment. & Decision Tree \\ [PERSON] et al. (2013) [18] & With the estimator of the remaining life prediction error using an artificial neural network, they develop a method estimating the failure rate threshold corresponding to the lowest maintenance cost. & Artificial Neural Network \\ [PERSON] et al. (2014) [19] & They performed a study to extract features from a centrifugal pump using a continuous wavelet transform technique and to determine equipment failures with SVM for diagnosing equipment failures with vibration data. & Continuous Wavelet Transform, SVM \\ [PERSON] and [PERSON] (2014) [20] & They proposed a diagnostic system using artificial neural networks and wavelet transformations to efficiently diagnose mechanical failures such as mass imbalance and misalignment of axes that occur frequently in wind power generation systems. & Artificial Neural Network, Wavelet Transform \\ [PERSON] et al. (2016) [21] & Using the marine environmental data, they suggested a method for estimating the remaining life of a pump tower, the main structure of LNG FPSO. & K-means clustering, Autoregressive Integrated Moving Average \\ [PERSON] et al. (2018) & They proposed an agent-based inference engine and a multi-agent system collaborative module for car failure diagnosis. & Agent-based inference engine based on a forward chaining algorithm \\ [PERSON] et al. (2020) [23] & They reviewed predictive data mining methods for fault detection and diagnosis in electrical equipment. & Review study \\ [PERSON] et al. (2021) [24] & They reviewed the research works related to Fault Detection and Diagnosis (FDD) methods. & Review study \\ \hline \hline \end{tabular}
\end{table}
Table 3: Previous works on failure diagnosis.