## 3 Case Study: Structural Factor Analysis for Public Transportation Bus
### Preprocessing Work for Maintenance Records  
To enhance the quality and usability of the maintenance data, a systematic preprocessing work was implemented. The preprocessing steps addressed key challenges associated  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No DM** & **Job Position** & **Experience (Years)** & **Age** & **Educational Level** \\ \hline DM \#1 & Head of the Depot & 26 & 51 & MSc \\ DM \#2 & Senior Engineer & 20 & 45 & MSc \\ DM \#3 & Senior Engineer & 18 & 42 & MSc \\ DM \#4 & Technical Engineer & 5 & 25 & BSc \\ DM \#5 & Technical Engineer & 4 & 27 & BSc \\ \hline \end{tabular}
\end{table}
Table 3: Profiles of the invited DMs.  
with data inconsistencies, missing values, and non-standardized terminology. The preprocessing workflow ensured data integrity, consistency, and relevance.  
The initial phase of preprocessing focused on text standardization and terminology normalization to address inconsistencies arising from manual data entry and colloquial language use. All text entries were converted to lowercase to eliminate case sensitivity issues, thereby ensuring uniformity across the dataset. Special characters and extraneous spaces were removed to further clean the text data. Additionally, common abbreviations and workshop jargon were standardized using a predefined glossary of vehicle-specific terminology. This step also involved correcting obvious spelling mistakes in component names and repair actions to enhance data accuracy.  
The second phase addressed missing data, which is a common issue in manually documented maintenance records. Records were first screened to identify instances with missing critical information. Entries with excessive missing values were removed to maintain data integrity. For the remaining records, domain knowledge was leveraged to fill in missing values where possible. This approach ensured that incomplete records were supplemented with realistic and contextually appropriate data.  
The third phase of preprocessing involved data categorization to enhance the structure and usability of the dataset. A standardized coding system was developed to classify maintenance activities, ensuring consistency across the dataset. Similar repair actions were grouped into standardized categories, simplifying analysis and comparison. Components were classified into hierarchical systems, reflecting their functional relationships within the vehicle structure. Unified failure mode descriptions were created to standardize the representation of faults.  
The fourth phase of preprocessing focused on data integration to consolidate and enrich the dataset. Related maintenance records were merged to create comprehensive entries for each maintenance event, ensuring that all relevant information was captured in a single record. The integrated dataset was then cross-referenced with vehicle operational data to provide contextual information and enhance the dataset's relevance for predictive analysis. Duplicate entries were identified and consolidated to eliminate redundancy, ensuring that each maintenance event was represented uniquely.  
The final phase of preprocessing involved organizing the preprocessed data into a structured format suitable for analysis. Standardized fields were created to ensure consistency across the dataset, and proper data relationships were established to maintain integrity.  
Through this comprehensive preprocessing framework, the maintenance dataset was transformed into a high-quality, structured resource suitable for advanced analysis. The establishment of a standardized terminology and structured relationships enables precise tracking of component failure histories and identification of failure correlations. The enhanced data quality and structured organization facilitate more accurate insights into maintenance patterns and component reliability, contributing to improved maintenance planning and decision-making processes.