Instrument Fault Diagnosis Research Articles

Intelligent instrument fault diagnosis and prediction system based on digital twin technology

In the context of informatization and intelligent manufacturing systems, digital twin technology provides new technical ideas for intelligent decision-making. Aiming at instruments with high cost and low output, this paper develops low-cost high-efficiency fault diagnosis system to realize rapid feedback and fault of fault diagnosis results. The system includes three layers: data layer, control layer and output layer. In the data layer, this uses MEMS sensors and Zigbee wireless transmission network to construct a data link the physical end and the virtual model. In the control layer, this paper stores the collected twin data through cloud technology, extracts and calls relevant data according to the function module and then maps it to the output layer. In the output layer, this paper constructs a characteristics interpretation system, which divides the test set and training set through the dynamic database, to complete the classifier and results output. The results are fed back to the judgment framework and control layer to evaluate the effects of fault diagnosis and prediction, which provides a new method for model optimization.

On-line multiple component and instrument fault diagnosis in dynamic systems : M.N. Barkesseh, N.A. Kheir, pp 815–821

On-line multiple component and instrument fault diagnosis in dynamic systems : M.N. Barkesseh, N.A. Kheir, pp 815–821

On-Line Multiple Component and Instrument Fault Diagnosis in Dynamic Systems

This paper presents a methodology, for on-line implementation, to diagnose failure(s) when it occurs in physical plant component(s) and/or Instrument(s). This methodology allows detectingcomponent faults by a bank of matched filters. The detection of instrument failures is accomplished by a bank of full-order Luenberger .observers. A rule-based technique is also developed and used to Identify a failure. Simulation results of an aircraft model have shown that this diagnostic approach is applicable to a large number of failures, and the action to remove these failures is also possible via an automated process.

Parameter-insensitive technique for aircraft sensor fault analysis

A new method of analyzing faults in the m measurements of an nth-order system is presented. The proposed approach uses the estimation error space of each observer in a bank of observers to detect and isolate sensor faults. The designs are applied to a nonlinear model of an unmanned aircraft that has been described in previous publications. The reconfigurability of the aircraft sensor system is demonstrated, and the results show rapid recovery from a faulty sensor. The use of the observation error eliminates the need for state-space computations, thus producing an effective real-time fault monitor for fly-by-wire aircraft. N an earlier paper,1 a comparison of two techniques of instrument fault diagnosis (IFD) was made. This work is an extension of Patton and Willcox's idea of using analytical redundancy to design a match between m components of the observation error space instead of using state estimates di- rectly as discussed by Clark,3'4 Clark and Setzer,5 Frank and Keller,6 and Watanabe and Himmelblau.7 IFD in dynamic systems has received a significant amount of attention recently.211 Most methods described in the liter- ature discuss the analytical redundancy approach in prefer- ence to the use of redundant hardware. Analytical redundancy provides redundant (estimate) information from different measurements of a process, usually with observer or Kalman filter schemes. The commonly discussed state estimate solu- tion to IFD is based on the principle of generating estimates of part or all of the system state vector from subsets of the measurements, which when compared with similar estimates from other observers can be used to monitor the health of an instrument. The problem with the state estimate solution to IFD arises as the observer requires a good linear model of the process, and it must also be assumed that the disturbances on the system are well modeled or else have an insignificant effect on plant parameter variations. These limitations cause the state estimate approach to be inadequate for many real en- gineering applications. Sensitivity to input-induced parameter variations causes uncertain errors between redundant state estimate vectors, and in an IFD scheme these errors could cause false signaling of an instrument fault. It becomes clear that the bandwidth of uncertain signals should be estimated prior to the IFD system design. The use of frequency domain sensitivity information in this way enables a robust approach to the observer design to be made. The conjecture used is that the innovations or prediction error signals contain all the information concerning the parameter variations of the pro- cess being identified and controlled. Attention is thus turned toward the use of an innovations-based approach to system fault diagnosis that has wide potential applications. By using a weighting of the measurement estimation error as a parity