Pages that link to "Item:Q2170729"
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The following pages link to Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes (Q2170729):
Displaying 11 items.
- Fault identification in industrial processes using an integrated approach of neural network and analysis of variance (Q460038) (← links)
- Data preprocessing method and fault diagnosis based on evaluation function of information contribution degree (Q1629560) (← links)
- Fault diagnosis with evolving fuzzy classifier based on clustering algorithm and drift detection (Q1665489) (← links)
- Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques (Q1796632) (← links)
- A combined Markov chain model and generalized projection nonnegative matrix factorization approach for fault diagnosis (Q1993069) (← links)
- Fault detection strategy combining NARMAX model and Bhattacharyya distance for process monitoring (Q1996655) (← links)
- Unlocked decision making based on causal connections strength (Q2068564) (← links)
- Fuzzy model-based fault detection and diagnosis for a pilot heat exchanger (Q3006197) (← links)
- Early malfunction diagnosis of industrial process units utilizing online linear trend profiles and real‐time classification (Q4562525) (← links)
- A K-means/support vector machine based self-adaptive online fault diagnosis method for fuel cell systems (Q5196972) (← links)
- Fault detection and isolation of multi-variate time series data using spectral weighted graph auto-encoders (Q6168965) (← links)
