A Non-intrusive Three-way Catalyst Diagnostics Monitor Based on Support Vector Machines - Assignment Example

October 19, A Non-intrusive Three-way Catalyst Diagnostics Monitor Based on Support Vector Machines Introduction The speaker, Dr. Emit Backey, discussed the Ford motor entity’s current non-intrusive diagnostics monitoring. The monitoring is grounded on the three way catalyst diagnostic process. The paper discusses the company’s support vector process. Evidently, the Ford Entity maximized the use of support vector machines to successfully set into motion the non-intrusive 3 way catalyst diagnostic monitor.

Diagnostic Monitoring Dr. Emit Backey’s expertise includes the complicational Intelligence and Modern Controlled Methods engineering concept (Backey 2). The concept is grounded on implementing non-classical remedies to the different problems arising in any given situation (Backey 2). One such situation is the engineering production process. For example, the Ford Mustang GT uses the engineered sensor to determine catalyst output (Backey 3). The sensor and catalyst ensures motor vehicle exhaust fumes adhere to the government’s environmental standards (Backey 3).

Furthermore, Dr. Backey explained the current monitoring process contributes to the control of polluting exhaust fume contents. Ford Company’s ODB monitor motor vehicle maintenance device enhances the monitoring and control of the exhaust contents. The easily developed new device implements a non-intrusive maintenance process (Backey 4), not interfering with the current control strategy. The easy development and the non-interference concept effectively contribute to better control and diagnostic procedures (Backey 4).

Moreover, the Ford entity implements the clustering strategy. The company prefers the FCM cluster technology. Under the technology, the entity takes into account the FCM cluster strategy. Different cluster technologies are taken into consideration. One clustering strategy is the K-means statistical tool approach. The MATLAB statistical tool is another good alternative (Backey 11). The company strategy is a continuing research and development plan, continually developing data that will enhance the Ford Company’s motor vehicle operations.

In addition, the Ford entity must implement the above bucket style diagnostic strategy. The strategy is a compulsory American government requirement. Failure to comply with the requirement, the company cannot sell its motor vehicles within the United States environment (Backey 5). The sensor must be tailored to fit the different motor vehicle models. Further, the current strategy incorporates physics principles (Backey 6). The physics equations contributed to the strategy’s success.

The equations were combined with black box technology (Backey 6). Under the technology, various inputs are placed in the box. Different Black box inputs generate different outputs. Using the gray box concept, less physics concepts are used. The company implements the fuel mass factor-based BFSO strategy (Backey 6) Furthermore, the Ford entity implements the XBM support vector strategy. The strategy focuses on the different data forms’ generating different relevant decisions. The XBM strategy is very popular strategy within the biological engineering segment and the biological physics realm (Backey 7).

Under the current strategy, the current motor vehicle engine burning volume strategy is very effective, generating 100% accuracy. The tests produced the expected 100 percent rating. The strategy perfectly works on limousines and similar stretch motor vehicles (Backey 12). Conclusion The above discussion shows the Ford entity’s implementing the non-intrusive diagnostic monitoring. The monitoring is grounded on the three way catalyst diagnostic process. The company’s support vector units enhance the monitoring process.

Clustering had been successfully set into motion. Evidently, the Ford Entity maximized the use of support vector machines to successfully implement the non-intrusive 3 way catalyst diagnostic monitor.Works CitedDr.Makki, Imad. "A Non-intrusive Three-way Catalystic Diagnostic Monitor BAsed on Support Vector Machines." 2014.