Feature signature prediction of a boring process using neural network modeling with confidence bounds

Prediction of machine tool failure has been very important in modern metal cutting operations in order to meet the growing demand for product quality and cost reduction. This paper presents the study of building a neural network model for predicting the behavior of a boring process during its full life cycle. This prediction is achieved by the fusion of the predictions of three principal components extracted as features from the joint time–frequency distributions of energy of the spindle loads observed during the boring process. Furthermore, prediction uncertainty is assessed using nonlinear regression in order to quantify the errors associated with the prediction. The results show that the implemented Elman recurrent neural network is a viable method for the prediction of the feature behavior of the boring process, and that the constructed confidence bounds provide information crucial for subsequent maintenance decision making based on the predicted cutting tool degradation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45845/1/170_2005_Article_114.pd

Bayesian approach to measurement scheme analysis in multistation machining systems

Different measurement schemes in multistation machining systems carry different amounts of information about the root causes of dimensional machining errors. The choice of a measurement strategy in a multistation machining system is therefore crucial for subsequent successful identification of the machining error root causes. Recent advances in the linear state-space modelling of dimensional errors in multistation machining processes facilitate a formal and systematic characterization of measurement schemes. In this paper, the stream-of-variation methodology is employed to characterize various measurement schemes quantitatively in multistation machining systems using the Bayesian approach in statistics. Application of these methods is demonstrated in the characterization of measurement schemes in the machining process used for machining of an automotive cylinder head. </jats:p

Immune Systems Inspired Approach to Anomaly Detection, Fault Localization and Diagnosis in Complex Dynamic Systems

Almost prevalent use of electronics, complicated software, new materials, and technologies makes fault diagnosis and management in contemporary engineering systems increasingly difficult to deal with. Unavoidable design defects, quality variations in the production process, as well as different usage patterns, make it infeasible to foresee all possible faults that may occur on a given system. As a result, traditional precedent-based diagnostic approaches offer a very limited diagnostic coverage based on testing only for the a priori known or anticipated failures, often falsely presuming that the system is operating normally if the full set of diagnostic tests pass. To circumvent these difficulties and provide a more complete coverage for detection and localization of the source of any fault, a new paradigm for design of diagnostic systems is needed. An approach inspired by the functionalities and characteristics of natural immune systems is presented and discussed here. The capability of the newly proposed paradigm to isolate the source of an anomaly without the need to train with signatures characterizing the underlying fault is demonstrated in the simulations of a diesel engine Exhaust Gas Recirculation (EGR) system and a generator portion of a commercially available marine diesel-generator system.Center for Electromechanic