  Context: As Industrial Cyber-Physical Systems (ICPS) become more connected
and widely-distributed, often operating in safety-critical environments, we
require innovative approaches to detect and diagnose the faults that occur in
them. Objective: We profile fault identification and diagnosis techniques
employed in the aerospace, automotive, and industrial control domains. By
examining both theoretical presentations as well as case studies from
production environments, we present a profile of the current approaches being
employed and identify gaps. Methodology: A scoping study was used to identify
and compare fault detection and diagnosis methodologies that are presented in
the current literature. Results: Fault identification and analysis studies from
127 papers published from 2004 to 2019 reveal a wide diversity of promising
techniques, both emerging and in-use. These range from traditional
Physics-based Models to Data-Driven Artificial Intelligence (AI) and
Knowledge-Based approaches. Predictive diagnostics or prognostics featured
prominently across all sectors, along with discussions of techniques including
Fault trees, Petri nets and Markov approaches. We also profile some of the
techniques that have reached the highest Technology Readiness Levels, showing
how those methods are being applied in real-world environments beyond the
laboratory. Conclusions: Our results suggest that the continuing wide use of
both Model-Based and Data-Driven AI techniques across all domains, especially
when they are used together in hybrid configuration, reflects the complexity of
the current ICPS application space. While creating sufficiently-complete models
is labor intensive, Model-free AI techniques were evidenced as a viable way of
addressing aspects of this challenge, demonstrating the increasing
sophistication of current machine learning systems.(Abridged)
