TY - GEN

T1 - A new unsupervised approach to fault detection and identification

AU - Costa, Bruno

AU - Angelov, Plamen

AU - Guedes, Luiz Affonso

PY - 2014/7

Y1 - 2014/7

N2 - In this paper, a new fully unsupervised approach to fault detection and identification is proposed. It is based on a two-stage algorithm and starts with the recursive density estimation (RDE) in the feature space. The choice of the features is important and in the real world process that we consider these are control and error related variables. The basis of the proposed approach is the fully unsupervised evolving classifier AutoClass which can be seen as an extension of the earlier one, but is using data clouds and data density information. It has to be stressed that the density in the data space is not the same as the well-known and widely used in statistics probability density function (pdf) although it looks similar. The density in the data space, D is pivotal and instrumental for anomaly detection. It can be calculated recursively, which makes it very efficient in terms of memory, computational power and, thus, applicable to on-line applications. Importantly, the proposed method not only can detect anomalies, but also can identify and diagnose the fault during the second stage of the process. While the first stage is centred around RDE, the second stage is based on the evolving fuzzy rule-based (FRB) classifier AutoClass. A key advantage of AutoClass is that it is fully unsupervised (there is no need to pre-specify the fuzzy rules, number of classes) and can start learning "from scratch". AutoClass can be initialised with some prior knowledge (assuming that it does exists) and evolve/develop it further, but that is not mandatory. This new approach is generic, but in this paper without limiting the concept it is validated on a lab based control kit. In this particular example, the features are the control and error signals. The results significantly outperform alternative methods, which is in addition to the advantages that the approach is autonomous.

AB - In this paper, a new fully unsupervised approach to fault detection and identification is proposed. It is based on a two-stage algorithm and starts with the recursive density estimation (RDE) in the feature space. The choice of the features is important and in the real world process that we consider these are control and error related variables. The basis of the proposed approach is the fully unsupervised evolving classifier AutoClass which can be seen as an extension of the earlier one, but is using data clouds and data density information. It has to be stressed that the density in the data space is not the same as the well-known and widely used in statistics probability density function (pdf) although it looks similar. The density in the data space, D is pivotal and instrumental for anomaly detection. It can be calculated recursively, which makes it very efficient in terms of memory, computational power and, thus, applicable to on-line applications. Importantly, the proposed method not only can detect anomalies, but also can identify and diagnose the fault during the second stage of the process. While the first stage is centred around RDE, the second stage is based on the evolving fuzzy rule-based (FRB) classifier AutoClass. A key advantage of AutoClass is that it is fully unsupervised (there is no need to pre-specify the fuzzy rules, number of classes) and can start learning "from scratch". AutoClass can be initialised with some prior knowledge (assuming that it does exists) and evolve/develop it further, but that is not mandatory. This new approach is generic, but in this paper without limiting the concept it is validated on a lab based control kit. In this particular example, the features are the control and error signals. The results significantly outperform alternative methods, which is in addition to the advantages that the approach is autonomous.

U2 - 10.1109/IJCNN.2014.6889973

DO - 10.1109/IJCNN.2014.6889973

M3 - Conference contribution/Paper

SN - 9781479966271

SP - 1557

EP - 1564

BT - Neural Networks (IJCNN), 2014 International Joint Conference on

PB - IEEE

ER -