Industrial Technology Research Institute

Chuang-Hua Chueh

Po-Yu Huang

Jia-Min Ren

Sen-Chia Chang

Yu-Hsiuan Chang

Chung-Wei Lin

Hsiang-Tsung Kung

Hsin-Chi Chen

Te-Ming Chen

Shu-Hsuan Lin

Hsin-Lung Hsieh

Chun-Hsu Chen

Chi-Heng Lin

Chun-Fang Chen

Cheng-Wei Chen

A parameter optimization device includes a data acquisition module, a sampling function calculation module, a clustering module and a parameter recommendation module. The data acquisition module is configured to acquire several input parameter values and corresponding several measurement output values. The sampling function calculation module is configured to obtain several sampling function values according to the input parameter values and the measurement output values. The clustering module is configured to obtain several parameter groups according to the input parameter values and the sampling function values. The parameter recommendation module is configured to obtain several recommended parameter values from at least one of the parameter groups.

Parameter optimization device and non-transitory computer readable medium

A data processing system, including a cyclic correlation establishing module, a data pattern establishing module, and a data pattern alignment module, is provided. The cyclic correlation establishing module receives a plurality of first sensor data, obtained from a first sensor operation performed on processing devices, and receives a table of processing steps and cyclic procedures. The cyclic correlation establishing module obtains a data correlation of the first sensor data according to the number of sample points in a data cycle of the first sensor data and the table to correct the first sensor data. The data pattern establishing module obtains a plurality of first data pattern features from the first sensor data. The data pattern alignment module aligns a plurality of second sensor data obtained from a second sensor operation performed on the processing devices with the first sensor data according to the first data pattern features.

Data processing system and method

An ensemble learning prediction method includes: establishing a plurality of base predictors based on a plurality of training data; initializing a plurality of sample weights of a plurality of sample data and initializing a processing set; in each iteration round, based on the sample data and the sample weights, establishing a plurality of predictor weighting functions of the predictors in the processing set and predicting each of the sample data by each of the predictors in the processing set for identifying a prediction result; evaluating the predictor weighting functions, and selecting a respective target predictor weighting function from the predictor weighting functions established in each iteration round and selecting a target predictor from the predictors in the processing set to update the processing set and to update the sample weights of the sample data.

Ensemble learning predicting method and system

A system and method for predicting remaining lifetime of a component of equipment is provided. The prediction system includes a data module, a feature module, a current data-based prediction module, a historical data-based prediction module, and a confidence module. The data module obtains a test sensor data of the component of equipment. The feature module obtains a historical health indicator and the current-health indicator. The current data-based prediction module obtains a first predicted remaining lifetime and a first prediction confidence according to the current-health indicator. The historical data-based prediction module obtains a second predicted remaining lifetime and a second prediction confidence according to the historical health indicator. The confidence module generates a final predicted remaining lifetime of the component of equipment according to the first predicted remaining lifetime, the second predicted remaining lifetime, the first prediction confidence and the second prediction confidence.

System and method for predicting remaining lifetime of a component of equipment

The disclosure is directed to an ensemble learning prediction apparatus. The apparatus includes a loss module, a diversity module, a sample weight module, and an integrating weight module. The loss module, the diversity module and the sample weight module calculate a loss, a diversity and a sample weight, respectively. An ensemble weight is learned by an object function built by the loss, diversity and the sample weight. The integrating weight module calculates an adaptive ensemble weight by integrating the ensemble weight and previous ensemble weights at a plurality of previous time points.

Ensemble learning prediction apparatus and method, and non-transitory computer-readable storage medium

A prognostic method and a prognostic apparatus for a processing apparatus are provided. In the steps of the prognostic method, multiple sensor data of a component of the processing apparatus and a heterogeneous data are obtained, multiple health indicators of the component are obtained by the multiple sensor data, a regressive characteristic function and an adjustment function are obtained according to the health indicators, the adjustment function corresponds to the heterogeneous data, a prediction function of health indicator is obtained according to the regressive characteristic function and the adjustment function, and a predictive value of health indicator is obtained according to the prediction function of health indicator to estimate a usage status of the component.

Prognostic method and apparatus for a processing apparatus

A health assessment method and a health assessment device of a workpiece processing apparatus are disclosed. The health assessment method includes the following steps. Acquire a first sensing data related to the workpiece processing apparatus at an operation stage of the workpiece processing apparatus. Set the first sensing data as a substitution of a first transform model to acquire a virtual workpiece quality. Set the virtual workpiece quality as a substitution of a second transform model to acquire a first virtual apparatus health index.

Health assessment method and health assessment device for workpiece processing apparatus

A system and a method for predicting a remaining useful life (RUL) of a component of an equipment are provided. The system for predicting the RUL of the component of the equipment includes a data acquisition unit, a feature capturing unit, a mapping function generating unit, a similarity analyzing unit and a RUL calculating unit. The feature capturing unit obtains an estimation feature according to a real time sensing record, and obtains a plurality of training features according to a set of history sensing records. The similarity analyzing unit obtains k similar features which are similar to the estimation feature according to the training features. The RUL calculating unit obtains at least one of k predicting information via a mapping function according to the k similar features and calculates an estimation RUL according to at least one predicting value.

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Taipei, Taiwan

Chuang-Hua Chueh