Fault Identification Model Research Articles

Gear Fault Diagnosis Method Based on Cyclostationary Degree and HMM

This paper proposes a gear fault diagnosis method based on cyclostationary degree and Hidden Markov Model (HMM) theory. By using the demodulation characteristic of cyclostationary degree for AM and FM signals, we extract the characteristic information of the gear working status. This information can be converted into sets of fault feature vector, which is used as the training sample or observed sample of HMM model for gear fault identification. Experimental results show that the gear fault diagnosis method has good recognition result for the four kinds of status of normal, broken teeth, pitting and wear gear.

Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques

With modern data collection system and computers used for on-line process monitoring and fault identification in manufacturing processes, it is common to monitor more than one correlated process variables simultaneously. The main problems in most multivariate control charts (e.g., T 2 charts, MCUSUM charts, MEWMA charts) are that they cannot give direct information on which variable or subset of variables caused the out-of-control signals. A Decision Tree (DT) learning based model for bivariate process mean shift monitoring and fault identification is proposed in this paper under the assumption of constant variance-covariance matrix. Two DT classifiers based on the C5.0 algorithm are built, one for process monitoring and the other for fault identification. Simulation results show that the proposed model can not only detect the mean shifts but also give information on the variable or subset of variables that cause the out-of-control signals and its/their deviate directions. Finally a bivariate process example is presented and compared with the results of an existing model.

Probabilistic model for sensor fault detection and identification

AbstractA new Bayesian belief network (BBN) model with discretized nodes is proposed for fault detection and identification in a single sensor. The single‐sensor model is used as a building block to develop a BBN model for all sensors in the process considered. A new fault detection index, a fault identification index, and a threshold setting procedure for the multisensor model are introduced. The fault detection index exploits the probabilistic information of the multisensor model, which, along with the proposed threshold setting procedure, leads to effective detection of faulty sensors. The fault identification index uses only the probabilistic information of the faulty sensor to determine in a discretized fashion the size of faults that should be analyzed within a moving time window to identify the fault type. Single‐sensor model design parameters (prior and conditional probability data) are optimized to achieve maximum effectiveness in detection and identification of sensor faults. The single‐sensor model and optimal values of design parameters are used to develop a multisensor BBN model for a polymerization reactor at steady‐state conditions. Its capabilities to detect and identify bias, drift, and noise in sensor readings are illustrated for single and simultaneous multiple faults by several case studies.

Physiological response to an emotional challenge is related to parental care received in juvenile Goeldi's monkeys ( Callimico Goeldii)

Model-based diagnosis enables efficient and safe operation of engineered systems. In this paper, we describe two algorithms based on a qualitative event-based fault isolation framework augmented with model-based fault identification that are applied to spacecraft power distribution systems. Although based on a common framework, the fundamental difference between the two algorithms is that one uses a global model for residual generation, fault isolation, and fault identification; whereas the other uses a set of minimal submodels computed using Possible Conflicts. We describe the implementation of the two algorithms and compare their diagnosis results on a representative spacecraft power distribution system.