Troubleshooting

Troubleshooting is a form of problem solving, often applied to repair failed products or processes on a machine or a system. It is a logical, systematic search for the source of a problem in order to solve it and make the product or process operational again. Troubleshooting is needed to identify the symptoms. Determining the most likely cause often involves a process of elimination to systematically rule out potential issues. Finally, troubleshooting requires confirmation that the solution restores the product or process to its working state. A strategy is an organized set of activities expressing a plausible way of achieving a goal. Strategies should not be viewed as algorithms, inflexibly followed to solutions. Problem solvers behave opportunistically, adjusting activities within a strategy and changing strategies and tactics in response to information and ideas.

In general, troubleshooting is the identification or diagnosis of "trouble" in the management flow of a system caused by a failure of some kind. The problem is initially described as symptoms of malfunction, and troubleshooting is the process of determining and remedying the causes of these symptoms.

A system can be described in terms of its expected, desired or intended behavior (usually, for artificial systems, its purpose). Events or inputs to the system are expected to generate specific results or outputs. (For example, selecting the "print" option from various computer applications is intended to result in a hardcopy emerging from some specific device). Any unexpected or undesirable behavior is a symptom. Troubleshooting is the process of isolating the specific cause or causes of the symptom. Frequently the symptom is a failure of the product or process to produce any results. (Nothing was printed, for example). Corrective action can then be taken to prevent further failures of a similar kind.

The methods of forensic engineering are useful in tracing problems in products or processes, and a wide range of analytical techniques are available to determine the cause or causes of specific failures. Corrective action can then be taken to prevent further failure of a similar kind. Preventive action is possible using failure mode and effects (FMEA) and fault tree analysis (FTA) before full-scale production, and these methods can also be used for failure analysis.

There are two major elements required to enable a troubleshooting diagnosis to take place - à priori domain knowledge and search strategies. These are interdependent, and here is where we can identify fundamentally two different types of problem, with matching approaches to their diagnosis. Rasmussen suggested there is strategy guided by the characteristics of the correct functioning of the device (topographic strategy), and strategy guided by the characteristics of abnormal functioning (symptomatic strategy). The second is really asking “what’s wrong?” the first is asking “what’s happening?”

A strategy is an organized set of activities expressing a plausible way of achieving a goal. Strategies should not be viewed as algorithms, inflexibly followed to solutions. Problem solvers behave opportunistically, adjusting activities within a strategy and changing strategies and tactics in response to information and ideas.

A symptomatic strategy (also known as case-based reasoning, or shallow reasoning) requires à priori domain knowledge that is gleaned from past experience which established connections between symptoms and causes. This knowledge is referred to as shallow, compiled, evidential, history-based as well as case-based knowledge. This is the strategy most associated with diagnosis by experts. Diagnosis of a problem transpires as a rapid recognition process in which symptoms evoke appropriate situation categories. An expert knows the cause by virtue of having previously encountered similar cases. Case-based reasoning is the most powerful strategy, and that used most commonly. However, the strategy won’t work independently with truly novel problems, or where deeper understanding of whatever is taking place is sought. A topographic strategy falls into the category of deep reasoning. With deep reasoning, in-depth knowledge of a system is used. Topography in this context means a description or an analysis of a structured entity, showing the relations among its elements. Also known as reasoning from first principles, deep reasoning is applied to novel faults when experience-based approaches aren’t viable. The topographic strategy is therefore linked to à priori domain knowledge that is developed from a more a fundamental understanding of a system, possibly using first-principles knowledge. Such knowledge is referred to as deep, causal or model-based knowledge.

Hoc noted that symptomatic approaches may need to be supported by topographic approaches because symptoms can be defined in diverse terms. The converse is also true – shallow reasoning can be used abductively to generate causal hypotheses, and deductively to evaluate those hypotheses, in a topographical search.