Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas. 

/pdf/a-review-on-vibration-based-condition-monitoring-of-rotating-1rwkagxg.pdf

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Research progress on oil-immersed transformer mechanical condition identification based on vibration signals

This paper develops generalized sparse filtering (GSF) by applying general norm normalization to improve the feature learning ability. A rotating machinery fault diagnosis method is then developed by combining the GSF and softmax regression. A rolling bearing dataset is applied to validate the performance of the developed method. The influences of normalization parameters on the diagnostic performance are investigated in detail, and thus, the best parameter combinations are determined based on the diagnostic accuracy and computing time. A planetary gearbox dataset is also applied to further validate the diagnostic performance on rotating machinery. Finally, the mechanism of the GSF is explained using a simple example. The results show that the GSF has a more powerful feature learning capacity than standard sparse filtering, and the developed method can obtain excellent diagnostic performance. Two variants of the developed method are recommended for the rotating machinery fault diagnosis.

Generalized sparse filtering for rotating machinery fault diagnosis

Condition monitoring and predictive maintenance of the equipment is an important topic of production environments, and many equipment manufacturers offer data services and build service business upon them. To further advance Metso’s capabilities, the configuration on how the data collected at the field, and how it is moved to the cloud for further processing and analysis, needs more advanced solutions. Typical equipment manufacturer’s customers from all over the world have numerous pieces of equipment on their sites. In this paper Arrowhead Framework and dynamic service discovery provided by it, are evaluated as a potential technology that could help machine and equipment providers to achieve better configurability of its data collection devices at the edge of the network.

Evaluating Arrowhead Framework for Dynamic Condition Monitoring Applications and Edge Computing in Mining

Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the collected vibration signals were transformed into images using the continuous wavelet transform. Subsequently, an MSCNN was designed to extract all detailed features of the original images. The final feature maps were obtained by fusing multiple feature layers. The parameters in the network were randomly generated and remained unchanged, which could effectively accelerate the calculation. Finally, an extreme learning machine was used to classify faults based on the fused feature maps, and the potential relationship between the fault and labels was established. The effectiveness of the proposed method was confirmed. This method performs better in mechanical fault diagnosis and classification than existing methods. 

/pdf/application-of-multi-scale-convolutional-neural-networks-and-3uza2037.pdf

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

High data throughput during real-time vibration monitoring can easily lead to network congestion, insufficient data storage space, heavy computing burden, and high communication costs. As a new computing paradigm, edge computing is deemed to be a good solution to these problems. In this paper, perceptual hashing is proposed as an edge computing form, aiming not only to reduce the data dimensionality but also to extract and represent the machine condition information. A sub-band coding method based on wavelet packet transform, two-dimensional discrete cosine transform, and symbolic aggregate approximation is developed for perceptual vibration hashing. When the sub-band coding method is implemented on a monitoring terminal, the acquired kilobyte-long vibration signal can be transformed into a machine condition hash occupying only a few bytes. Therefore, the efficiency of condition monitoring can benefit from the compactness of the machine condition hash, while comparable diagnostic and prognostic results can still be achieved. The effectiveness of the developed method is verified with two benchmark bearing datasets. Considerations on practical condition monitoring applications are also presented.

/pdf/perceptual-vibration-hashing-by-sub-band-coding-an-edge-53y2yaxs8e.pdf

Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring

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Perceptual Vibration Hashing by Sub-Band Coding: An Edge Computing Method for Condition Monitoring