Bearings are fundamental mechanical components in rotary machines (engines, gearboxes, generators, radars, turbines, etc.) that have been identified as one of the primary causes of failure in these machines. This makes bearing fault diagnosis (detection, classification, and prognosis) an economic very relevant topic, as well as a technically challenging one as evaluated by the extensive research literature on the subject. This paper employs a systematic methodology to identify, summarize, analyze, and interpret the primary literature on fuzzy formalisms for bearing fault diagnosis from 2000 to 2017 (March). The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis (summary, classification, and critique) of the available approaches resorting to fuzzy formalisms in this trendy topic. A discussion on a new promising future research direction is provided. A comprehensive list of references is also included.

A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis

Autonomous vessels: State of the art and potential opportunities in logistics

Observer-biased bearing condition monitoring

Industrial reheating furnaces: A review of energy efficiency assessments, waste heat recovery potentials, heating process characteristics and perspectives for steel industry

Centrifugal compressor fault diagnosis based on qualitative simulation and thermal parameters

In the present paper the design and implementation of a Fault Diagnosis system for a compression's process integrated in an IGCC (Integrated Gasification & Combined Cycle) section of a refinement plant is described. Both single and multiple faults have been considered which may cause errors in the sensor readings and/or in the actuators used in the process. A multivariable data-driven approach, that is a principal component analysis (PCA) technique has been adopted for monitoring the chemical process performances. A new procedure for the determination of number of principal components based on the statistical test ANOVA is introduced which constitutes the original contribution of the paper. The proposed approach on detection and isolation of faults have been tested and validated on the plant and its goodness and effectiveness could be proven.

Applications of Fault Diagnosis techniques for a multishaft centrifugal compressor

A new approach to fault detection and isolation that combines Principal Component Analysis (PCA), Clustering and Pattern Recognition is presented. Single, multiple faults which may cause errors in the sensor readings and/or in the actuators as well as process faults are considered. Determination of the number of principal components is based on the statistical test ANOVA following the approach proposed by the authors in previous works. To overcome to the growth of complexity in the analysis of process faults that typically involve many variables, an automatic procedure for the isolation of the principal known faults has been developed. The proposed methodology which is based on Clustering and Pattern Recognition Analysis represents the new contribution of the present paper. The method is tested on experimental data from an IGCC (Integrated Gasification & Combined Cycle) section of an oil refinery plant to monitor a compression's process. Results show the goodness and effectiveness of the proposed approach on process faults detection and isolation.

FDI of process faults based on PCA and cluster analysis

Principal Component Analysis based on ANOVA Test for Multishaft Centrifugal Compressor Fault Detection and Diagnosis

In this paper, an Advanced Process Control system based on a two-layer linear Model Predictive Control strategy is proposed. The control system aims at optimizing a pusher type billets reheating furnace, located in an Italian steel plant. A first principles nonlinear model has been developed, in order to obtain estimations of billets temperature inside the furnace. A Linear Parameter-Varying model for billets temperature has been accordingly derived. To obtain a global modellization of the furnace unit, an additional black-box approach has been adopted for the internal process dynamics. The overall resulting model has been exploited for the design of the Model Predictive Control scheme. Performances on an industrial process have shown the major profitability of the proposed control solution with respect to the previous one, based on a suitable handling of local PID controllers. In particular, significant energy saving has been obtained, together with an improved specifications fulfillment.

Optimization of a pusher type reheating furnace: An adaptive Model Predictive Control approach

In this work, the control optimization of a rotary kiln located in a cement plant is described. The need to enhance the efficiency level, to increase the profitability as well as the commitment to meet precise production standards has motivated the adoption of Model Predictive Control techniques. The adopted system architecture is composed of two different optimization layers. A suitable interaction policy has been developed so as to improve control performances and to meet possibly variable economic goals. The advantages of the proposed architecture are shown by significant simulations. The developed predictive control system has been implemented on real plant and its overall performances are compared to the performances of the previous standard PID control system. Very satisfactory system performances have been achieved been able to attain both economic benefits and minimization of environmental noxious emissions.

G. Astolfi

Papers

Applications of Fault Diagnosis techniques for a multishaft centrifugal compressor

FDI of process faults based on PCA and cluster analysis

Principal Component Analysis based on ANOVA Test for Multishaft Centrifugal Compressor Fault Detection and Diagnosis

Optimization of a pusher type reheating furnace: An adaptive Model Predictive Control approach

Application of Advanced Process Control techniques for a cement rotary kiln