目前筆記型電腦維修流程中僅專注於排除客戶送修時所反饋之故障問題，並未針對筆電其他部分進行相關性檢查與維護保養措施，使得筆電存在著再次返修之可能性。又針對電競筆電使用者而言，電競筆電所使用之零件價格通常較為高昂，若有多次返修之情形發生，勢必須付出較多的維修成本，容易造成使用者產生厭煩感，進而影響售後服務品質並導致顧客滿意度下降。因此本研究希望透過隱藏馬可夫模型搭配循序樣式探勘探討筆記型電腦較常發生之更換零件序列。
本研究個案為國內知名電腦硬體製造商，並由該個案公司維修服務中心提供筆記型電腦維修資料。為了方便後續研究進行，將維修資料中筆電送修次數進行整理並將所換修之零件加以分類。首先運用隱藏馬可夫模型中Baum-Welch演算法及Viterbi演算法並找出不同故障問題序列所對應之更換零件序列。接著應用Yen's Algorithm找出多條更換零件序列。最後運用循序樣式探勘中Aprioriall演算法找出筆記型電腦較常發生之更換零件序列。
經由本研究所提出筆記型電腦維修與更換零件推估之參考資訊可作為服務人員於維修判斷時的輔助資訊，使得維修人員能事先知道後續可能損壞之零件，因而能進行初步檢查並確認相關零件的使用情形，以降低故障再次發生之機率、減少筆電再次返修之可能性、提升筆電售後服務品質並達到筆記型電腦維修與預防性保養之目的。

Currently, the laptop computer maintenance process only focuses on troubleshooting the existed problems, the inspection of related possible defect components are not carried. And hence make the laptop have the possibility of re-repairing and affect the service satisfaction. This research proposes an approach integrating hidden Markov chain and process mining methods to identifying the frequent pattern of defect components based on a gaming laptop maintenance dataset form a leading laptop manufacturer in Taiwan.
According to the data of failure problems and related defect components, the research applied Baum-Welch and Viterbi algorithms to construct the hidden Markov model of the failed components. Then, Yen’s algorithm was used to identify replacement parts for different sequences of fault problems based on the replaceable components data. Finally, a sequential pattern mining approach “Aprioriall” was used to find out the replacement parts sequence that occurs more frequently on gaming laptops.
The research results can be used to support the service people to identify the possible defect parts in advance and carried out possible preventive maintenance. The possibility of re-repair can be reduced and increase the service satisfaction.

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