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PREFACE:

The general field of condition monitoring has received substantial attention over the last few decades and it is worth reflecting on the state of the topic, because, although it has always been practised at some level, the manner in which condition is assessed is constantly under review in the light of recent developments in understanding. 

In assessing the condition of a piece of equipment, the operator gathers data such as vibration, operating temperature, noise, performance and electrical parameters where appropriate.

 At one time, the comparison with normal condition was achieved largely on the basis of staff experience, but the general trend has been towards a more precise quantified approach. This has been required by revised patterns of working and increasing plant complexity, but it is, in essence, the same operation. 

A fundamental question arises of how one can ‘codify’ the knowledge of an experienced engineer and focus the knowledge on a specific area of plant. 

This presents indeed a challenge which has shown significant progress in recent years, although the issue cannot be regarded as completely resolved. 

Important progress has been made in computational modelling (both finite element analysis (FEA) and computational fluid dynamics (CFD)), artificial neural networks (ANNs), statistical approaches, expert systems and identification methods. 

All of these have a role to play in assessing the condition of a piece of equipment and their role will be outlined in subsequent chapters. First of all however, the general field of condition monitoring, as applied to rotating machines, is reviewed.

Generally, these two terms are linked under the general heading of condition monitoring, but in fact, there are two quite distinct functions. In both areas, the first requirement is to gather and record all salient details of the operation of the piece of equipment but, as will be discussed, the choice as to what details are salient is a far from trivial task. 

However, that discussion is deferred for the present. To illustrate this point with a specific example, let us consider a centrifugal pump driven by an electric motor. 

In such a case, the monitored parameters would include bearing vibration levels, temperature, water pressure, water flow rate, motor current and voltage. Note that although this is a fairly long list, it is by no means exhaustive.

 In some circumstances, one may wish to record the rotor vibration (as opposed to that of the bearings) and bearing oil temperature. In fact, even a relatively simple piece of machinery may have a significant number of parameters which may be useful for monitoring purposes and a judicious choice is required to limit the measured set to cost-effective proportions; however, making this choice requires some appreciable physical insight. 

Having decided on a set of monitored parameters, some method of recording is the next choice to be made and this ranges from regular spot checks to some form of continuous monitoring, now almost invariably computer based.

 Whilst the latter represents a more expensive option, it does offer more flexibility in terms of the ways in which data can be manipulated to offer insight into the underlying features of machine operation. Here again decisions are required which demand physical insight into machine operation and the likely failure scenarios. 

We now consider some of the ways in which plant data may be analysed and how this may be used to form judgements about plant operation. Clearly, any general trend in the plant data, or indeed a sudden change, suggests that the equipment has changed in some way and, subject to some checks, may require the removal of the plant from service. Note that realistically, all equipment is subject to some random perturbations and so, statistical techniques are needed to form a valid decision such as when to remove plant from service.