Principal component analysis technique for early fault detection

Abstract

Online condition monitoring and predictive maintenance are crucial for the safe operation of equipments. This paper highlights an unsupervised statistical algorithm based on principal component analysis (PCA) for the predictive maintenance of industrial induced draft (ID) fan. The high vibration issues in ID fans cause the failure of the impellers and, sometimes, the complete breakdown of the fan-motor system. The condition monitoring system of the equipment should be reliable and avoid such a sudden breakdown or faults in the equipment. The proposed technique predicts the fault of the ID fan-motor system, being applicable for other rotating industrial equipment, and also for which the failure data, or historical data, is not available. The major problem in the industry is the monitoring of each and every machinery individually. To avoid this problem, three identical ID fans are monitored together using the proposed technique. This helps in the prediction of the faulty part and also the time left for the complete breakdown of the fan-motor system. This helps in forecasting the maintenance schedule for the equipment before breakdown. From the results, it is observed that the PCA-based technique is a good fit for early fault detection and getting alarmed under fault condition as compared with the conventional methods, including signal trend and fast Fourier transform (FFT) analysis.

Reliability, availability, and condition monitoring of inverters of grid‐connected solar photovoltaic systems

“…The collected data set is smoothed or normalized to get the accuracy of the results. The loading matrix is formed using the covariance matrix of the data set $$D_{n\times m}$$ where n and m indicate the number of rows and columns of the data set matrix A, respectively, as discussed in [17]. The first step in the PCA algorithm is to subtract the mean from each dimension as shown in Equation ().…”

“… $$$$\begin{equation} D_{modified}=[D]_m-[\bar{D}]_m. \end{equation}$$$$Now, the covariance matrix is to be formed using the formula given in Equation () [17]. $$$$\begin{equation} [C]=\sum _{i=1}^p\frac{(d_i-\bar{d}) \times (d_i-\bar{d})^T}{n}, \end{equation}$$$$where $$\bar{d}$$ is the mean value of $$d_i$$.…”

“… $$$$\begin{equation} [C]=\sum _{i=1}^p\frac{(d_i-\bar{d}) \times (d_i-\bar{d})^T}{n}, \end{equation}$$$$where $$\bar{d}$$ is the mean value of $$d_i$$. The eigenvalues and eigenvectors are calculated, and eigenvectors are stored in a matrix [B], which contains the loading vectors shown in Equations () and () [17]. $$$$\begin{equation} ([C]_{m \times m}-[I]_{m \times m} \lambda )\lbrace D\rbrace _{m \times 1}=\lbrace 0\rbrace , \end{equation}$$$$where 'I' is representing the identity matrix.…”

Application of Multidimensional Statistical Methods to the Hydrochemical Study with R Software

“…There are fewer or the same number of principal components as there were original variables. The total variance or inertia of a data set is the information it holds [29]. The goal of PCA is to find the principal axes or principal components [30], otherwise known as the directions along which the data vary most.…”

Rotary Machines Fault Diagnosis based on Principal Component Analysis

“…The result demonstrated that the proposed method successfully identified healthy, unbalance and parallel misalignments of rotary rotor. Three identical induced draft fans were monitored together using an unsupervised statistical algorithm based on PCA [16]. It was observed that the PCA based technique is a good fit for early fault detection compared to the conventional methods.…”