Multivariate Time Series Analysis for Scientific Instrument Degradation

Design a comprehensive predictive maintenance framework for high-precision scientific instruments using time series decomposition. Create a workflow that integrates sensor data from multiple sources, handles missing values in heterogeneous datasets, and develops a machine learning model to predict potential equipment failure with >90% accuracy. Include feature engineering techniques specifically tailored to scientific instrumentation drift, incorporating temporal dependencies and anomaly detection algorithms.