Service and Delivery
Knowledge Element: Diagnostics fault finding methods
Definition of Knowledge Element:
Know the main Logical and analytical approaches to fault finding
A very brief summary of the Knowledge:
The most common method is called the half split method. This involves isolating major areas of the system to prove which half is OK and which is faulty. The same technique can be applied to the faulty half.
Select the text below for further study material:
Introduction to fault finding methods
A summary of expert opinion:
The knowledge named 'Diagnostics fault finding methods' is considered to be very important.
In addition, the knowledge is thought to be very difficult to replace, it is mostly learned from experience, and Between 7% and 30% of Group are Experts.
It is also quite specialised knowledge, and quite stable knowledge.
See Parameter table below for more detail.
Risk:
This knowledge element has a risk value of 7.23, whilst the average risk for the whole knowledge structure is 4.862. Consult the RISK page for more details.
This knowledge is one of the higher risk items in the knowledge structure. Its risk value of 7.23, can be compared with the average risk for the highest ten percent of the knowledge, of 7.412 and the average risk for the whole map stated above.
Knowledge linked by learning dependency:
| Diagnose faults |
| Diagnostics fault finding methods |
|---|
Postrequisite knowledge implies that Diagnostics fault finding methods must be understood before for a person can gain a full understanding of the knowledge listed in the top row of the Linked Knowledge table.
Diagnostics fault finding methods has no prerequisite knowledge items and is therefore known as a leaf node. A leaf node is knowledge that has not been investigated further when the map was constructed.
Parameters assigned for Diagnostics fault finding methods.
| Identifier | Name | Value | Clarifier | Weight | Study Mean |
P1 |
Importance |
8.0 |
very important |
1.0 |
6.34 |
P2 |
Recovery |
8.0 |
very difficult to replace |
1.0 |
3.38 |
P3 |
Study-Exp |
7.0 |
mostly learned from experience |
1.0 |
3.46 |
P4 |
Known By |
1.0 |
Between 7% and 30% of Group are Experts |
-1.0 |
2.80 |
P6 |
Specialised |
5.0 |
quite specialised knowledge |
1.0 |
5.05 |
P7 |
Stability |
4.0 |
quite stable knowledge |
-0.7 |
6.41 |
People Involved with Diagnostics fault finding methods:
| Person | Expert | Capable | Responsible | N S Jones | --- |
Yes |
--- |
M A Smith | --- |
Yes |
--- |
E N Roberts | --- |
Yes |
--- |
S P Evans | Yes |
Yes |
--- |
A A Pendragon | Yes |
--- |
--- |
|---|
Computed Similarity
The most similar node to Diagnostics fault finding methods is Site survey.
The most dissimilar node to Diagnostics fault finding methods is Organise Accommodation.
Computer generated Options for Action:
Options for Action are computer generated ideas for the development of a particular knowledge area that an analyst or manager may wish to consider. The options generated for Diagnostics fault finding methods are listed below.
Option Class is :- Staff development
This option is :- Formal training provided internally using internal experts and resources
This briefly involves :- Training provided by internal experts may be worthwhile in certain areas of high knowledge risk. It can help protect and deploy critical company specialist knowledge and also help experts to review and develop this knowledge.
Option Class is :- Protect, Develop Deploy
This option is :- The formal study and analysis of knowledge using Knowledge Structure Mapping
This briefly involves :- The knowledge element in question can be taken as the initial investigative question for a further knowledge study using KSM. The results can be treated separately and also combined with the current study in an integrative manner considering all learning dependency links.