Page 1082 - The Mechatronics Handbook

identify the undesired state. Deductive analyses, such as fault tree analysis (FTA), involve investigation
of possible desired state of the overall system and identify the component states that contribute to the
occurrence of the undesired state, i.e., describe how the undesired state is achieved.
The event tree method is a pictorial representation of all the events (success or failures) that can occur
in a system. Similar to other techniques, the event tree method can be used for systems in which all
subsystems/components are continuously operating. This method is also widely used for systems in which
some or all of the subsystems/components are in a standby mode with sequential operational logic and
switching, such as safety oriented systems (Billinton and Allan, 1983).
FMEA is a bottom-up qualitative technique used to evaluate a design by identifying possible failure modes
and their effects on the system, occurrence of the failure modes, and detection techniques. The history of
FMEA goes back to the early 1950s when the technique was utilized in the design and development of flight
control systems (Dhillon, 1983). Since then it has been widely used in the industry for specific designed
systems with known knowledge of their components, subsystems, functions, required performance and
characteristics, and so on. Criticality analysis (CA) is a quantitative method used to rank critical failure
mode effects by taking into consideration the probability of their occurrence. FMECA is a design technique
composed of FMEA and CA and provides a systematic approach to clarify hardware failures.
Fault tree analysis (FTA) is a top-down procedure which considers components in working or failed
states, and it has been proven difficult to handle degraded component states. FTA can be used to obtain
minimum cut sets, which define the modes of system failures and identify critical components. The
reliability measures for the top event of FTA can be obtained provided that the failure data on primary
events/failures is available.

39.3 Failure Analysis of Mechatronic Systems

The failure modes of a mechatronic system include failure modes of mechanical, electrical, computer,
and control subsystems, which could be classified as hardware and software failures. The failure analysis
of mechatronic systems consists of hardware and software fault detection, identification (diagnosis),
isolation, and recovery (immediate or graceful recovery), which requires intelligent control.
The hardware fault detection could be facilitated by redundant information on the system and/or by
monitoring the performance of the system for a given/prescribed task. Information redundancy requires
sensory system fusion and could provide information on the status of the system and its components,
on the assigned task of the system, and the successful completion of the task in case of operator error or
any unexpected change in the environment or for dynamic environment.
The simplest monitoring method identifies two conditions (normal and abnormal) using sensor
information/signal: if the sensor signal is less than a threshold value, the condition is normal, otherwise
it is abnormal. In most practical applications, this signal is sensitive to changes in the system/process
working conditions and noise disturbances, and more effective decision-making methods are required.
Generally, monitoring methods can be divided into two categories: model-based methods and feature-
based methods. In model-based methods, monitoring is conducted on the basis of system modeling and
model evaluation. Linear, time-invariant systems are well understood and can be described by a number
of models such as state space model, input–output transfer function model, autoregressive model, and
autoregressive moving average (ARMA) model. When a model is found, monitoring can be performed
by detecting the changes of the model parameters (e.g., damping and natural frequency) and/or the
changes of expected system response (e.g., prediction error). Model-based monitoring methods are also
referred to as failure detection methods.
Model-based systems suffer from two significant limitations. First, many systems/processes are non-
linear, time-variant systems. Second, sensor signals are very often dependent on working conditions.
Thus, it is difficult to identify whether a change in sensor signal is due either to the change of working
conditions or to the deterioration of the process.
Feature-based monitoring methods use suitable features of the sensor signals to identify the operation
conditions. The features of the sensor signal (often called the monitoring indices) could be time and/or

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