Fault Diagnosis and Prognosis in Rotating Machines Carried Out by Means of Model-Based Methods: A Case Study

2013 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Diagnostic Methods ◽  
Rotating Machines ◽  
Machine Model ◽  
Large Power ◽  
Machine Vibration ◽  
Vibration Data ◽  
Machine Maintenance ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

Diagnostic methods, based on mathematical models, can be used to identify the most common faults and malfunctions of rotating machines by minimizing the error between experimental vibration data and the corresponding theoretical response of the rotor system caused by a specific set of excitations. These techniques allow the severity and location of the fault to be estimated. Moreover, depending on the fault characteristics, model-based prognostic techniques can be used to study appropriate corrective actions that can eliminate the cause of the malfunctions or reduce the machine vibration levels. This paper shows the results of a diagnostic analysis carried out to investigate the cause of the high vibration of the HP-IP steam turbine of a large power unit that occurred, during the runups, when approaching the first balance resonance. The numerical results confirmed the suspect that this high vibration was caused by a shaft bow. Then, the machine model was used also to study and optimize a corrective action that allowed the operating speed to be reached and the shaft bow to be eliminated by means of the turbine heating caused by a load rise. The successful results obtained with the machine maintenance carried out considering the indications provided by the model-based diagnostic and prognostic analyses are shown and discussed.

Effects of the Shaft Normal Modes on the Model-Based Identification of Unbalances in Rotating Machines

2011 ◽  
Author(s):  
A. Vania ◽  
P. Pennacchi ◽  
S. Chatterton
Keyword(s):  
Normal Modes ◽  
Fault Identification ◽  
Diagnostic Strategy ◽  
Rotating Machines ◽  
Large Power ◽  
Identification Methods ◽  
Model Based ◽  
Rotating Machine ◽  
Experimental Response ◽  
Innovative Techniques

Model-based methods can be applied to identify the most likely faults that cause the experimental response of a rotating machine. Sometimes, the objective function, to be minimized in the fault identification method, shows multiple sufficiently low values that are associated with different sets of the equivalent excitations by means of which the fault can be modeled. In these cases, the knowledge of the contribution of each normal mode of interest to the vibration predicted at each measurement point can provide useful information to identify the actual fault. In this paper, the capabilities of an original diagnostic strategy that combines the use of common fault identification methods with innovative techniques based on a modal representation of the dynamic behavior of rotating machines is shown. This investigation approach has been successfully validated by means of the analysis of the abnormal vibrations of a large power unit.

scholarly journals Model-based fault diagnosis and prognosis of dynamic systems: a review

2019 ◽  
Vol 30 ◽  
pp. 435-442 ◽  
Author(s):  
Thushara Ekanayake ◽  
Devapriya Dewasurendra ◽  
Sunil Abeyratne ◽  
Lin Ma ◽  
Prasad Yarlagadda
Keyword(s):  
Fault Diagnosis ◽  
Dynamic Systems ◽  
Model Based ◽  
Diagnosis And Prognosis ◽  
Fault Diagnosis And Prognosis

Unbalance Identification in Large Steam Turbo-Generator Unit Using a Model-Based Method

2013 ◽  
Author(s):  
Paolo Pennacchi ◽  
Andrea Vania ◽  
Steven Chatterton ◽  
Ionel Nistor ◽  
Philippe Voinis ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Point Of View ◽  
Fault Identification ◽  
Machine Model ◽  
Machine Vibration ◽  
Shaft Line ◽  
Model Based ◽  
Turbo Generator ◽  
Complete Discussion ◽  
Condition Monitoring System

Fault identification in industrial machine is a topic of major importance under engineering point of view. In fact, the possibility to identify not only the type, but also the severity and the position of a fault occurred along a shaft-line allows quick maintenance and shorten the downtime. This is really important in the power generation industry where the units are often of several tenths of meters long and where the rotors are enclosed by heavy and pressure-sealed casings. In this paper, an industrial experimental case is presented related to the identification of the unbalance on a large size steam turbine of about 1.3 GW, belonging to a nuclear power plant. The case history is analyzed by considering the vibrations measured by the condition monitoring system of the unit. A model-based method in the frequency domain, developed by the authors, is introduced in detail and it is then used to identify the position of the fault and its severity along the shaft-line. The complete model of the unit (rotor – modeled by means of finite elements, bearings – modeled by linearized damping and stiffness coefficients and foundation – modeled by means of pedestals) is analyzed and discussed before being used for the fault identification. The assessment of the actual fault was done by inspection during a scheduled maintenance and excellent correspondence was found with the identified one by means of authors’ proposed method. Finally a complete discussion is presented about the effectiveness of the method, even in presence of a not fine tuned machine model and considering only few measuring planes for the machine vibration.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

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