online engineering degree/engineering degree online/online engineering courses/engineering technology online/engineering courses online/engineering technician degree online/online engineering technology/electronic engineering online
Rotating machines do not fail randomly. There are root-causes for each failure. Usually the condition of the failed part has been changed and leads to failure.
To stop failure, it is necessary to know why failure occurs. Based on failure analysis knowledge, critical components should be selected for monitoring. Proper parameters, sensors and set points need to be defined for condition monitoring. By being aware of the major reason for failure and by observing the condition of necessary components, a high level of reliability can be achieved. Most failures in predictive maintenance and trouble-shooting exercises occur because the entire power generation system is not considered. Defining complete power generation system (all components, systems and parts involved) is a very important step.
Major reasons for problems and failure include:
Changes operating conditions, including changes in operating procedure. This is the most important reason for power generation machine failure.
Installation and commissioning issues.
Design, fabrication and assembly problems.
Machine wear-out.
All power generation trains react to operation, network and plant requirements. They do what the operation requires. Dynamic rotating machines use high-speed rotating parts (such as blades) to convert fluid energy to mechanical power and drive generator. Reliability of these rotating parts (as well as the reliability of the machine's auxiliaries) is considerably affected by operating conditions. The machine loading, transmitted torques, generated power and auxiliary functioning are affected by operation and particularly electrical power demand and control algorithms.
As an example, demand for more electrical power may result in train overload. The reliability of machine components (bearings, seals and so on) is directly related to the reliability of the auxiliary systems. In many cases, the root-cause of the component failure is found in the supporting auxiliary system. Changes in auxiliary system supply temperature, resulting from cooling water temperature change (for water cooled systems) or ambient air temperature change (for air-coolers), can be the root-cause of component failure such as bearing failure in the case of extra-hot oil. Operational changes can have similar effects. Usually machine or component failure occurs because equipment is subjected to conditions that exceed design values.
Most machinery damage and wear occur during transient conditions such as start-up or shutdown conditions. During this time, the equipment is subject to rapid temperature, pressure and speed changes. In many cases, the root-cause of rotating machine mechanical damage is that the power required by the system exceeded the capability of the machine.
Based on experience, the main failure root-cause is a change in operating conditions. A second common failure mechanism is issues related to installation and commissioning. Design or manufacturing problems (including engineering errors, material problems, manufacturing defects and so on) are a third source of failure, although design problems usually show up shortly after startup. Rare cases exist where design problems manifest themselves after an extended operating time. However, the main cause of design problem is that the component is not designed for specified operating condition. Component wear-out is often the effect and not the root cause. Worn out bearings, seals, wear rings and so on are usually due to operating condition changes. Various bearings often suffer from assembly or installation problems.
Trouble-shooting is used to discover and eliminate the root cause of trouble. Incomplete facts and insufficient information are primary reasons for failed trouble-shooting exercises. Usually in the rush to define what the problem is, many trouble-shooting engineers do not take sufficient time to obtain all of the facts. All changes should be properly identified for all components in power generation system (and her it is important to consider all parts such as auxiliaries and so on). Equipment functions, particularly all component and sub-system functions, need to be clearly identified. It is necessary to include all groups such as operators, maintenance people, manufacturers, sub-vendors and related contractors in the exercise. It is also important to find all baselines related to the major parameters involved.
Consider the following guides for trouble-shooting investigation:
Carefully observe failed component(s), their conditions and mode of failure. Failed component inspection is critical. Vendor opinion and consultant advices should be considered. Also, define the problem clearly.
Find the unit history, particularly operating time before the failure and the history of previous failures, especially similar failures.
Identify unit parameters, particularly those related to failed component (prior to failure). Baseline conditions should be obtained or established. Operator's logs and reliability data are useful sources of information. Special attention is required for parameters exceeding normal value. Trends are always important.
Collect data of failed component supply-source (and fabrication sources), design, materials, manufacturing details as well as assembly data and tolerances.
Identify all changes, particularly changes in operation. Investigate unit piping, foundation and all surrounding facilities.
New modeling methods, advanced simulation techniques and numerical calculations play important roles in trouble-shooting and root-cause analysis. For example, steam turbine rotor rub to the casing is often reported. Realistic dynamic and thermal expansion simulations of rotor and inner casing are required for a root-cause analysis of such cases. For many reliability issues, an accurate finite element analysis (FEA) of the machine is necessary to find real root-cause. Short cuts or simplified models may result in an erroneous conclusion.