# Adaptive Imputation of Irregular Truncated Signals with Machine Learning
## 6 Conclusions  
Our results demonstrate the effectiveness of ML-based data imputation methods for handling irregularly truncated signals, as well as the effectiveness of a popular ML classifier at correctly identifying the operating condition of a machine, as well as the type of fault if there is a fault present in the machine. Using these methods, we designed an adaptive data imputation system capable of distinguishing between MCAR, MAR, and MNAR data with between 88.85% and 100% accuracy, as well as determining the percentage of missing data present in a simulated dataset. Our system is also capable of switching the ML-based imputation method based on the missing data type and the percentage of missing data. From our experiments, we found that each of the examined imputation techniques performed reasonably well at imputing the missing data across all missing data mechanisms and percentages of missing data. However, in a simulated example where the dataset consisted of temperature, pressure, and vibration values, the imputation of missing pressure values results in significantly higher MAE and RMSE scores compared to temperature and vibration values, potentially indicating that pressure is an inherently more difficult value to impute compared to temperature and vibration, although it is worth noting that these results were obtained on a simulated dataset, and in order to conclusively claim this, more testing would need to be performed on real-world datasets and under more varied conditions. Another observation noted from the results is that across each of the three missing data mechanisms, both when evaluating the efficacy of the imputation and the effectiveness of the RF classifier, there is a slight uptick in performance when the percentage of missing data jumps from 30% to 31%. Given that this is a difference of a single percentage point, this uptick seems odd, but this is the point where the adaptive imputation system we designed switches the ML model used for imputation from KNN to MICE for MCAR and MAR data and MIMM to joint modeling for MNAR data, so a potential conclusion could be that KNN and MIMM are more effective at dealing with high levels of missingness compared to MICE and joint modeling. Further experimentation is required to determine if this trend proves true in other scenarios than the one presented in this paper. There is much potential for future work in this area of research. The adaptive imputation model presented in this paper was designed on a simulated dataset. For deployment to a real-world industrial environment, the methods used would need to revised to fit the characteristics of the data being collected by that organization. One of the pitfalls of current ML research for maintenance-related tasks is that the needs of different organizations can vary greatly. However, one of the benefits of an adaptive imputation system like the one presented in this paper is that if properly and fully implemented in an industrial environment, once the model is trained on the necessary data for that organization, it could prove to be a vital tool for maintenance engineers in ensuring the optimal operating conditions for their equipment. Another avenue for potential research is to examine even more types of ML models for this task. ML is a vast field of research, and it continues to grow over time, which means that newer, faster, more accurate models are released regularly, each of which could have great benefits for various industries. All in all, ML-based research in the area of CBM is becoming increasingly important as industries turn towards more digital operating processes, and tools such as the adaptive imputation technique described in this paper could greatly enhance operating procedures. Conceptualization, K.J. ; data curation, T.W. ; formal analysis, T.W. ; funding acquisition, K.J. and J.O.-M.; investigation, T.W. ; methodology, T.W. ; project administration, K.J. ; resources, K.J. and J.O.-M.; software, T.W.