Sensor Fault Detection with the Single Sensor Parity Relation

Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS), previously developed by the authors, is improved in this paper to detect and classify different fault classes. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called I-DCIDS, can identify seven classes of fault, i.e. out of range, stuck signal, dithering, standard deviation, trend coherence, spike and bias. Fault detection is performed by means of basic mathematical laws that require some user-defined input parameters, i.e. acceptability thresholds and windows of observation. This paper presents in detail the I-DCIDS methodology for sensor fault detection and classification. Moreover, this paper reports some examples of application of the methodology to simulated data to highlight its capability to detect sensor faults which can be commonly encountered in field applications.

Download Full-text

Discrete-Event Sensor Fault Isolation With Application to an Electro-Mechanical Break System

Dynamic Systems and Control ◽

10.1115/imece2002-39326 ◽

2002 ◽

Author(s):

Pierluigi Pisu ◽

Giorgio Rizzoni

Keyword(s):

Fault Detection ◽

Discrete Event ◽

Vehicle Control ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Control System Design ◽

Qualitative Modeling ◽

Sensor Fault ◽

Sensor Fault Detection ◽

Single Sensor

Fault detection and isolation has become one of the most important aspects in vehicle control system design. In this paper, a new method for single sensor fault detection and isolation for automotive on-board applications that combines model-based diagnostic and qualitative modeling approach is presented. A depth one algorithm for qualitative identification is given and applied to a electro-brake system.

Download Full-text

Sensor Fault Detection and Validation For Chemical Process Using A Neural Network Model

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)42904-1 ◽

1997 ◽

Vol 30 (11) ◽

pp. 561-566 ◽

Cited By ~ 1

Author(s):

Koji Morinaga ◽

Michael E. Sugars ◽

Koji Muteki ◽

Haruo Takada

Keyword(s):

Neural Network ◽

Fault Detection ◽

Network Model ◽

Neural Network Model ◽

Chemical Process ◽

Sensor Fault ◽

Sensor Fault Detection

Download Full-text

Sensor Optimal Configuration Method for Sensor Fault Detection

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327092 ◽

2020 ◽

Author(s):

Wen Yang

Keyword(s):

Fault Detection ◽

Optimal Configuration ◽

Sensor Fault ◽

Sensor Fault Detection

Download Full-text

Development of L1-norm sliding mode observer for sensor fault diagnosis of an industrial gas turbine

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651821996173 ◽

2021 ◽

pp. 095965182199617

Author(s):

Mahyar Akbari ◽

Abdol Majid Khoshnood ◽

Saied Irani

Keyword(s):

Fault Detection ◽

State Space ◽

Gas Turbine ◽

Sliding Mode ◽

State Space Model ◽

Sensor Fault ◽

Space Model ◽

Decision Logic ◽

Sensor Fault Detection ◽

Industrial Gas Turbine

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

Download Full-text