Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines

• Published in: International journal of advanced manufacturing technology 2021-06, Vol.114 (9-10), p.3077-3092
• Main Authors: de Sá Só Martins, Dionísio Henrique Carvalho, Viana, Denys Pestana, de Lima, Amaro Azevedo, Pinto, Milena Faria, Tarrataca, Luís, Lopes e Silva, Fabrício, Gutiérrez, Ricardo Homero Ramírez, de Moura Prego, Thiago, Monteiro, Ulisses Admar Barbosa Vicente, Haddad, Diego Barreto
• Format: Article
• Language: English
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Electronic devices; Engineering; Failure analysis; Failure detection; Faults; Industrial and Production Engineering; Machine learning; Mechanical Engineering; Media Management; Misalignment; Original Article; Rotating machinery; Rotating machines; Support vector machines; Vibration analysis
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-021-06873-2

Failure detection from mechanical vibration analysis is crucial in industry machinery, with early discovery allowing for preventive action to be performed. This paper introduces a prototype of an IoT system capable of (i) identifying combined failures of a rotating machine and (ii) predicting failures, in a non-invasive manner. An embedded solution is devised, which is able to classify four types of operating conditions, namely (i) normal, (ii) imbalanced, (iii) imbalanced associated with horizontal misalignment, and (iv) imbalanced associated with vertical misalignment. The goal of the paper is to propose an automatic method of diagnosis and measurement of combined failures in rotating machines. The employed methodology combines a simulation bench and measuring the severity in a controlled environment. Three distinct machine learning techniques were compared for classification purposes: support vector machines, k -nearest neighbors, and random forests. The results obtained reveal the possibility of differentiating between the types of combined faults; an accuracy of 81.41% using a random forest classifier was achieved. A supervisory system was developed which is responsible for monitoring machines and sending wireless alert messages. The latter are sent to a control application, allowing for user interaction through mobile devices. Results reveal the possibility of differentiating between the types of combined faults, and also motor failure severity profile for different scenarios. Through the construction of severity profiles, when faults occurred, high vibration values were registered at elevated speeds. The proposed methodology can be used in any rotating machine that complies with the conditions imposed by ISO 10816.