Diagnostics

Diagnostic is a process used to detect the failure mode and the failing component in a system. Furthermore, the goal of a diagnostic is to measure a certain indication of the failure function. Diagnosis is done after or during the process of fault occurring. Therefore, by conducting fault diagnosis, it is possible to improve the system’s reliability and availability. Fault diagnosis provides a more detailed insight into the nature and locality of the failure. The obtained information can be used to reduce system downtime and help schedule an adequate maintenance procedure [25]. By running a fault diagnosis at an early stage, it is possible to avoid any sudden failure of the overall system.

Machine fault diagnostic involves the transfer of the information obtained in the observation and/or features space into machine fault space [24]. The transition process between the spaces is usually referred to as pattern recognitions. Pattern recognition uses

graphical tools such as power spectra, phase spectra, spectrum graphs, AR spectra, spectrograms, wavelet spectrograms, wavelet phase graphs and many others. These techniques are usually time consuming. Therefore, automatic pattern recognition algorithms are more desirable, which can be achieved by sorting the signals according to the information and/or features extracted from them.

2.6.4 Prognostics

Prognostic is the process of predicting the remaining useful lifetime of a component or estimating the probability of failure of that component [26]. In order to achieve a zero- downtime performance more efficiently, prognostic approaches are preferred to diagnostics [24]. Moreover, to predict the future failure of a system, prognostic tools are employed to analyse the current and previous history of the system’s operating conditions. Also, the rate of deviation in the operating conditions from the normal conditions can be closely monitored to realise a prognostic tool.

Some of the major challenges in any system prognosis are the time-to-failure or remaining useful life (RUL) prediction, condition monitoring interval prediction and trust valued estimation (TVE) [23]. The RUL is often used to predict how long the component can operate before a failure occurs. On the other hand, condition monitoring interval is applied to predict the probability of a machine to operate with no fault nor failure up to a certain time in future.

Prognostic is different to a CBM in a sense that CBM determines any impending failure based on the physically measured indicators installed on the equipment, whereas, prognostic has the additional capability of predicting the equipment’s RUL. Furthermore, prognostic tends to factor any external stress factors such as environment causes when calculating the RUL. This is considered as another way of enhancing the condition- monitoring information. A prognostic system is able to predict a failure well in advance, when compared to the predictions made by the current state condition of the equipment [20].

2.6.5 Decision Making

A decision is made based on the data collection and data analysis. These decisions may change in operating routines or in maintenance planning, which may result in a need for

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additional data collection and analysis. Upon the completion of a decision, reports can be generated and archived for future references, and evaluations can be performed if required [26].

2.6.6 Usage-based Modelling

The concepts of CBM was developed in parallel to Health and Usage Monitoring Systems (HUMS). HUMS can detect an early fault and the need for maintenance actions, through processing of signals from sensors and inspections [27]. The RUL of a component can be predicted through usage-based models. In this approach, the model verification process plays an important role in developing and deploying the usage-based model [23].

Flowchart 2.1 shows the maintenance strategies and shows that CBM implementation will develop the management of the maintenance system.

Maintenance strategy

Corrective Maintenance Proactive

Preventive Maintenance FTA RCFA FMEA Unplanned Failure Run-to-Failure Reactive Small items Non Critical Unlikely to fail Temporary repair Refurbishment Replacement or modification Inspection and failure diagnosis Predictive CBM Interval Preventive Consumable replacement Failure pattern known

Random failure pattern PM induced failures Monitoring Testing Inspection

Optimization Process for Program

2.7 Condition Monitoring

The condition monitoring includes the monitoring of the operating behaviour of a system and analysing the gathered information and data. Furthermore, by making analysis of failure symptoms, CM identifies when and how the item is probable to fail. Condition monitoring encompasses a huge range of technologies and can be used to detect failure symptoms that would otherwise go undetected [9]. The condition monitoring serves a main purpose, to detect faults or degradation processes that have reached a certain characteristic threshold. By producing an indication of the type and severity of the fault in advance, the condition monitoring prevents any functional breakdown during the operation.

2.7.1 Temperature Measurement

Temperature measurement techniques such as, temperature-indicating paint and thermography are employed to detect potential temperature-related failures in equipment. A temperature change in a healthy equipment can be associated with several problems, such as, excessive mechanical friction (e.g., faulty bearings, inadequate lubrication), degraded heat transfer (e.g., fouling in a heat exchanger) and poor electrical connections (e.g., loose, corroded or oxidized connections) [3].

2.7.2 Dynamic Monitoring

In contrast to condition monitoring, dynamic monitoring makes use of techniques such as spectrum analysis and shock pulse analysis to measure and interpret the energy emitted from the equipment in the form of a vibration, pulse or acoustic wave. By analysing the vibration characteristics of particular mechanical equipment, faults and damages caused by

wear, imbalance and misalignment can be detected[3].

2.7.3 Oil Analysis

Oil analysis techniques such as ferrography and particle counter testing are often applied on different types of oil (e.g. lubrication, hydraulic or insulation oils) to provide an indication of an unhealthy machine. This particular technique allows for the detection of faults such as degradation, oil contamination, improper oil consistency and oil deterioration. The research carried out on oil analysis can be categorised into one of the following areas:

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• Fluid Physical Properties (Viscosity, appearance).

• Fluid Chemical Properties (TBN, TAN, additives, contamination, % water). • Machine Health (wear metals associated with plant components) [3].

2.7.4 Corrosion Monitoring

Corrosion monitoring techniques include coupon testing and corrometer testing, which are useful in detection of the extent, rate and state (i.e. active or passive) of corrosion in a particular material [3].

2.7.5 Non-destructive Testing

Non-destructive procedures such as X-ray and ultrasonic are online tests that are considered as non-invasive and can be implemented whilst the equipment is under operation [3].