## 5 Conclusions and Future Directions
### Future Directions  
Future directions are planned towards enhancing the failure identification and the reliability analysis of the HVAC systems in passenger cars by employing advanced machine learning techniques. An automated decision system can be developed to detect the failure mode and give alarm signals. The HVAC system should be continuously monitored for maintenance and repair work as it will help to reduce the failure modes. Effective implementation of these reliability improvement suggestions can surely enhance the reliability, performance, and durability of the HVAC system, reducing the frequency of failures and minimizing disruptions to passenger comfort and safety. The future scope can be investigations of the failure dependencies and common mode failures present in the analyzed HVAC system. The results obtained from the field failure data can also be compared with expert judgments and approaches such as FMEA. The improved reliability can also be improved using a new dataset after implementing the reliability improvement recommendations.  
Conceptualization, S.K.K., Methodology, S.K.K., M.S., S.A. and P.V.I.; Validation, S.A. and R.B.P.; Formal analysis, S.K.K.; Investigation, S.K.K. and R.B.P.; Data curation, S.A. and P.V.I.; Writing--original draft, S.K.K., M.S. and R.B.P.; Writing--review & editing, A.R., C.R.S. and R.B.P.; Supervision, A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
Appendix A Results of the Best-Fit Probability Distribution for Subsystems of the Analyzed HVAC System  
\begin{tabular}{l l l l l l l l l} \hline \hline Probability & \multicolumn{3}{c}{A/C Evaporator Front Side (AEFS)} & \multicolumn{3}{c}{Blower Front Side (BFS)} \\ \cline{2-9} Distribution & Ranking & LKV & BIC & AIC & Ranking & LKV & BIC & AIC \\ \hline Normal & 3 & \(-\)1064.7 & 2138.56 & 2133.41 & 7 & \(-\)852.87 & 1714.41 & 1709.75 \\ \hline
3P-Weibull & 3 & \(-\)1062.49 & 2138.71 & 2130.98 & 5 & \(-\)838.08 & 1689.15 & 1682.16 \\ \hline
2P-Weibull & 1 & \(-\)1061.8 & 2132.74 & 2127.59 & 2 & \(-\)873.93 & 1684.52 & 1679.86 \\ \hline Gamma & 2 & \(-\)1062.55 & 2134.25 & 2129.1 & 3 & \(-\)838.44 & 1685.53 & 1680.87 \\ \hline
1P-Exponential & 6 & \(-\)1102.24 & 2209.06 & 2206.48 & 1 & \(-\)838.69 & 1681.7 & 1679.37 \\ \hline
2P-Exponential & 5 & \(-\)1094.98 & 2199.12 & 2193.97 & 4 & \(-\)838.58 & 1685.81 & 1681.15 \\ \hline Lognormal & 4 & \(-\)1069.52 & 2148.18 & 2143.03 & 6 & \(-\)852.6 & 1713.87 & 1709.21 \\ \hline \multicolumn{9}{l}{Probability} & \multicolumn{3}{c}{A/C Gas Leak at Joints (AGLAI)} & \multicolumn{3}{c}{A/C Compressed (ACC)} \\ \cline{2-9} Distribution & Ranking & LKV & BIC & AIC & Ranking & LKV & BIC & AIC \\ \hline Normal & 5 & \(-\)386.9 & 780.91 & 777.8 & 1 & \(-\)361.1 & 729.14 & 726.21 \\ \hline
3P-Weibull & 4 & \(-\)372.48 & 755.62 & 750.95 & 2 & \(-\)360.71 & 731.82 & 727.42 \\ \hline
2P-Weibull & 3 & \(-\)373.17 & 753.46 & 750.34 & 3 & \(-\)364.14 & 735.21 & 732.42 \\ \hline Gamma & 2 & \(-\)373.09 & 753.3 & 750.19 & 4 & \(-\)367.21 & 741.34 & 738.41 \\ \hline
1P-Exponential & 1 & \(-\)373.26 & 750.08 & 748.53 & 5 & \(-\)370.54 & 744.54 & 743.07 \\ \hline
2P-Exponential & 1 & \(-\)371.86 & 750.83 & 747.72 & 6 & \(-\)370.41 & 747.75 & 744.82 \\ \hline Lognormal & 4 & \(-\)373.59 & 754.28 & 751.17 & 7 & \(-\)378.41 & 763.75 & 760.82 \\ \hline \hline \end{tabular}