Một bài tổng quan về chẩn đoán đa lỗi của các máy quay công nghiệp sử dụng kết hợp dữ liệu đa cảm biến

Artificial Intelligence Review - Tập 56 - Trang 4711-4764 - 2022
Shreyas Gawde1, Shruti Patil1,2, Satish Kumar1,2, Ketan Kotecha1,2
1Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University, Pune, Symbiosis (Deemed University), Pune, India
2Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Pune, Symbiosis (Deemed University), Pune, India

Tóm tắt

Các máy quay là một phần thiết yếu trong bất kỳ ngành công nghiệp sản xuất nào. Việc ngừng hoạt động đột ngột của những máy này do bảo trì không đúng cách có thể dẫn đến việc ngừng hoạt động của cả ngành. Thời đại cách mạng công nghiệp lần thứ 4 đang hình thành rõ rệt trong các chiến lược bảo trì, nổi bật là bảo trì dự đoán. Dự đoán và chẩn đoán lỗi là mối quan tâm chính trong bảo trì dự đoán, vì đây là vấn đề lớn mà tất cả các kỹ sư bảo trì đều gặp phải. Hầu hết các nghiên cứu tổng quan tài liệu bibliometric hiện có tập trung vào chẩn đoán lỗi trong các máy quay, chủ yếu chỉ tập trung vào một loại lỗi duy nhất. Tuy nhiên, vẫn chưa có một phân tích sâu sắc về tài liệu nào tập trung vào khía cạnh "chẩn đoán đa lỗi sử dụng dữ liệu đa cảm biến" của các máy quay. Liên quan đến điều này, bài báo này xem xét tài liệu về "chẩn đoán đa lỗi sử dụng kết hợp dữ liệu đa cảm biến" của các máy quay công nghiệp áp dụng các kỹ thuật học máy/học sâu. Một phương pháp bibliometric lai được sử dụng để phân tích các bài báo từ cơ sở dữ liệu "Web of Science" và "Scopus" trong 10 năm qua. Phương pháp phân tích tài liệu được sử dụng, là định lượng cũng như định tính, không chỉ áp dụng phương pháp truyền thống (phân tích bibliometric và mạng) mà còn một phương pháp mới mang tên ProKnow-C, bao gồm một số giai đoạn, bao gồm lọc thông minh và toàn diện từ một tập hợp lớn các kết quả và cuối cùng chọn lựa các bài báo có liên quan hơn đến chủ đề nghiên cứu. Dựa trên các ấn phẩm hiện có, bài báo thực hiện phân tích theo dữ liệu ấn phẩm theo năm, loại bài báo, phân bố ngôn ngữ của các bài báo, các nhà tài trợ tài chính, các liên kết, phân tích trích dẫn và mối quan hệ giữa các từ khoá, tác giả, v.v. để cung cấp cái nhìn sâu sắc về các xu hướng nghiên cứu trong lĩnh vực liên quan. Bài báo cũng tập trung vào các chiến lược bảo trì, các phương pháp bảo trì dự đoán, các thuật toán AI, kết hợp dữ liệu đa cảm biến, thách thức và hướng đi tương lai trong "chẩn đoán đa lỗi sử dụng kết hợp dữ liệu đa cảm biến" trong các máy quay.

Từ khóa

#máy quay #chẩn đoán đa lỗi #dữ liệu đa cảm biến #bảo trì dự đoán #học máy #học sâu

Tài liệu tham khảo

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