  Prognostics or early detection of incipient faults is an important industrial
challenge for condition-based and preventive maintenance. Physics-based
approaches to modeling fault progression are infeasible due to multiple
interacting components, uncontrolled environmental factors and observability
constraints. Moreover, such approaches to prognostics do not generalize to new
domains. Consequently, domain-agnostic data-driven machine learning approaches
to prognostics are desirable. Damage progression is a path-dependent process
and explicitly modeling the temporal patterns is critical for accurate
estimation of both the current damage state and its progression leading to
total failure. In this paper, we present a novel data-driven approach to
prognostics that employs a novel textual representation of multivariate
temporal sensor observations for predicting the future health state of the
monitored equipment early in its life. This representation enables us to
utilize well-understood concepts from text-mining for modeling, prediction and
understanding distress patterns in a domain agnostic way. The approach has been
deployed and successfully tested on large scale multivariate time-series data
from commercial aircraft engines. We report experiments on well-known publicly
available benchmark datasets and simulation datasets. The proposed approach is
shown to be superior in terms of prediction accuracy, lead time to prediction
and interpretability.
