Peter Ballé's research while affiliated with Technische Universität Darmstadt and other places
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Publications (25)
In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach...
In this contribution, an approach to model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. The process is decomposed into several subprocesses and for each a nonlinear model is identified. This model library consists of Takagi-Sugeno type fuzzy models and is used to generate several estim...
An approach for model-based fault detection and isolation (FDI) of
sensor and process faults for nonlinear processes is presented. The
process is decomposed into several sub-processes and for each process a
nonlinear model is identified. This model bank consisting of fuzzy
models (Takagi-Sugeno type) is used to generate several different
estimates...
The Transferable Belief Model (TBM) is an approximate reasoning approach which is derived from the Dempster–Shafer mathematical theory of evidence. The key property of TBM is its ability to treat inconsistency in data by a novel concept of assigning belief, referred to as the ‘open-world’ assumption. The aim of this paper is to apply TBM to a diagn...
In this paper, a new approach to model-based fault detection and isolation (FDI) for nonlinear processes is presented. A local linear fuzzy model of the process is used for the generation of structured parity equations. The model is run both in parallel and in series–parallel to the process, which leads to residuals with different sensitivities. Th...
An approach for model-based fault detection and isolation (FDI) of
sensor and process faults for nonlinear processes is presented. A fuzzy
model (Takagi-Sugeno type) of the nominal process provides
characteristic features like time constants and static gains in the
actual region of operation. Comparing these with features derived by
recursive param...
Heat exchangers play an important role in chemical and process
industries. In order to improve reliability and control performance,
intelligent concepts for control, supervision and reconfiguration are
necessary. In the paper, an approach is presented which integrates
model-based adaptive control and reconfiguration based on fault
detection/diagnos...
Local linear fuzzy models are used for fault detection and fault
diagnosis (FDD) for nonlinear processes. A Takagi-Sugeno type fuzzy
model of the nominal process is identified off-line and linearized in
the current operating point. In addition, a second linear model is
identified online by applying a recursive least-squares (RLS) algorithm.
The dev...
An approach for model-based fault detection and isolation (FDI) of
sensor and process faults for nonlinear processes is presented. The
process is decomposed into several subprocesses and for each a nonlinear
model is identified. This model bank consisting of fuzzy models
(Takagi-Sugeno type) is used to generate several different estimates for
proce...
The development of a reliable fault detection and isolation (FDI) scheme for nonlinear processes is often laborious and difficult to achieve due to the complexity of the system. Neural networks and fuzzy models, able to approximate nonlinear dynamic functions offer a powerful tool to cope with this problem. In this paper, a multi-model approach for...
In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach...
In this contribution, a new approach for model-based fault detection and isolation (FDI) of sensor faults for non linear processes is presented. A local linear fuzzy model of the process is used for the generation of structured residual equations similar to the parity space approach. The model is run in parallel and series-parallel to the process w...
Deals with identification of nonlinear processes and model-based
fault detection/isolation (FDI). The applicability of the proposed
methods is illustrated on a three-tank laboratory setup. The process
identification is based on the local linear model tree (LOLIMOT)
algorithm and leads to local linear models. The parameters of the local
models are u...
An approach for the diagnosis of faults in dynamic systems based
on a neuro-fuzzy scheme is presented. The simple structure that
represents fuzzy rules in a neural network uses a rule extraction
mechanism varying from most other approaches as it is based on concepts
of machine learning. An additional, straightforward optimization
eventually enhance...
Heat exchangers play an important role in chemical and process
industries. In order to improve reliability and control performance,
intelligent concepts for control, supervision and reconfiguration are
necessary. In this paper, an approach is presented which integrates
model-based adaptive control and reconfiguration based on fault
detection/diagno...
After a short overview of the historical development of model-based fault detection, some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated, based on the work within the IFAC SAFEPROCESS Technical Committee. Some basic fault-detection and diagnosis methods are briefly considered. Then, an evaluation...
After a short view on the historical development of model-based fault detection some proposals for the terminology in the field of supervision, fault detection and diagnosis are stated based on the work within the IFAC Technical Committee SAFEPROCESS. Some basic fault detection and diagnosis methods are briefly considered. Then, an evaluation of pu...
Citations
... FTCs are typically classified as an active FTC (AFTC) and a passive FTC (PFTC). AFTC approaches rely on a fault detection and identification (FDI) unit to explicitly detect and estimate faults, whereas PFTC approaches are designed to be robust against certain faults without the need for explicit detection [7,8]. The latter are more conservative than the AFTC, but they are less computationally expensive. ...
... The most common NF systems are based on two types of fuzzy models Takagi and Sugeno (1985a), Sugeno and Kang (1988) and Mamdani (1976), Mamdani and Assilian (1995) combined with NN learning algorithms. TS models use local linear models in the consequents, which are easier to interpret and can be used for control and fault diagnosis Füssel et al. (1997), Isermann and Ballé (1997). Mamdani models use fuzzy sets or rules as consequents and therefore give a more qualitative description. ...
Reference: Nonlinear Methods for Fault Diagnosis
... In general, many research efforts in the field of FDI have been made (Isermann and Balle, 19%;Patton, 1994;Isermann, 1997;Frank, 1990;Spreitzer and Balle, 2000;Blanke et al. (2003)). One FDI approach is the parity space approach. ...
... Faults in complex sensor systems can be defined as unexpected events that might occur at a certain point of time, which might trigger bigger events or a series of other unexpected events. According to Isermann and Balle [1], faults are defined as an unauthorized or allowed deviation of what is declared as normality of a defined system. ...
... Ein weiterer Steckbrief sei hier exemplarisch wiedergegeben: Hardware erfordert und für eine industrielle Umsetzung noch durch Modifikationen reduziert werden muss. Als einführende Literatur zur residuenbasierten Fehlererkennung und Diagnose eignet sich auch [40]. Berichte von erfolgreichen praktischen Anwendungen der Fehlerdiagnose mit (teilweise selbstlernenden) Fuzzy-Ansätzen finden sich z. ...
... Unfortunately, in many situations the function of the underlying data generation process is unknown and this leads to problems. For example, an explicit mathematical description (model) of the output signal is a mandatory condition for implementing Kalman tracking algorithms [2][3][4], data fusion in multi-sensor systems [4][5][6][7], fault detection and diagnosis [8][9][10], etc. In such cases, we usually seek for a new function , simpler than , but close enough to it so that useful information from the measurements can be extracted by calculations performed on . ...
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... So werden in [150] Beobachter auf Basis von TSK Fuzzy-Systemen zur Fehlerdiagnose in dynamischen Systemen entworfen. Des Weiteren wurde das bereits vorgestellte Neuro-Fuzzy-System Lolimot zur modellbasierten Fehlerdiagnose eingesetzt [16]. Ähnliche Methoden finden sich u.a. in [122,218]. ...
... Described procedure shows good results for step-like faults, other faults have not been considered and it is not possible to reconstruct the damaged signal. Balle [3] applied a fuzzy modeling approach to detect sensor faults in a thermal platform. The scheme uses linear Tagaki-Sugeno fuzzy models to generate parity relations. ...
... In this paper, we propose a novel approach, not pursued before, which is able to account for the influence of perturbations on the evaluated EIS characteristic and the equivalent circuit model by a statistical approach. Properly quantified uncertainties lead to diagnostic solutions that, rather than making a fault statement in clear yes/no categories, suggest the probability that a particular fault is present [6]. This is essential for a cautious and more reliable diagnosis of SOFCs, which can be additionally enhanced with the operator's prior knowledge. ...
... The automatic generation of fuzzy rules for nonlinear systems and for practical applications has been introduced since 1997. [6][7][8][9] Fuzzy rules are automatically generated 10-12 by computational methods (e.g., dataclustering) has been applied for autonomous vehicles. For example, a method known as the SC i.e., subtractive clustering is utilized for the extraction of fuzzy rules from data. ...
