数据不完备下基于Informer的离心鼓风机故障趋势预测方法
TH17; 离心鼓风机在运行过程中,监测数据缺失会导致故障趋势预测滞后和预测精度下降.针对该问题,提出一种考虑数据不完备的离心鼓风机故障趋势预测方法.首先,基于张量分解对缺失监测数据进行填补,获得离心鼓风机的完备监测数据;其次,基于填补后的完备监测数据利用深度置信网络(DBN)构建能表征离心鼓风机健康状态的健康指标;最后使用Informer方法预测健康指标的未来走势,实现离心鼓风机的故障趋势预测.案例分析结果表明,相比缺失数据,利用填补后的数据所建立的预测模型能更早预测故障的发生,同时所提出的预测方法较Transformer、长短时记忆(LSTM)和门控循环单元(GRU)等常用传统方法预测精度...
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| Published in | 计算机集成制造系统 Vol. 29; no. 1; pp. 133 - 145 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
重庆大学 机械传动国家重点实验室,重庆 400044%重庆大学 机械与运载工程学院,重庆 400044
31.01.2023
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-5911 |
| DOI | 10.13196/j.cims.2023.01.012 |
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| Abstract | TH17; 离心鼓风机在运行过程中,监测数据缺失会导致故障趋势预测滞后和预测精度下降.针对该问题,提出一种考虑数据不完备的离心鼓风机故障趋势预测方法.首先,基于张量分解对缺失监测数据进行填补,获得离心鼓风机的完备监测数据;其次,基于填补后的完备监测数据利用深度置信网络(DBN)构建能表征离心鼓风机健康状态的健康指标;最后使用Informer方法预测健康指标的未来走势,实现离心鼓风机的故障趋势预测.案例分析结果表明,相比缺失数据,利用填补后的数据所建立的预测模型能更早预测故障的发生,同时所提出的预测方法较Transformer、长短时记忆(LSTM)和门控循环单元(GRU)等常用传统方法预测精度更高. |
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| AbstractList | TH17; 离心鼓风机在运行过程中,监测数据缺失会导致故障趋势预测滞后和预测精度下降.针对该问题,提出一种考虑数据不完备的离心鼓风机故障趋势预测方法.首先,基于张量分解对缺失监测数据进行填补,获得离心鼓风机的完备监测数据;其次,基于填补后的完备监测数据利用深度置信网络(DBN)构建能表征离心鼓风机健康状态的健康指标;最后使用Informer方法预测健康指标的未来走势,实现离心鼓风机的故障趋势预测.案例分析结果表明,相比缺失数据,利用填补后的数据所建立的预测模型能更早预测故障的发生,同时所提出的预测方法较Transformer、长短时记忆(LSTM)和门控循环单元(GRU)等常用传统方法预测精度更高. |
| Author | 张友 钱静 李聪波 易茜 林利红 |
| AuthorAffiliation | 重庆大学 机械传动国家重点实验室,重庆 400044%重庆大学 机械与运载工程学院,重庆 400044 |
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| Author_FL | ZHANG You YI Qian LIN Lihong QIAN Jing LI Congbo |
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| Keywords | 离心鼓风机 故障趋势预测 Informer方法 不完备数据 张量分解 |
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| PublicationYear | 2023 |
| Publisher | 重庆大学 机械传动国家重点实验室,重庆 400044%重庆大学 机械与运载工程学院,重庆 400044 |
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| Title | 数据不完备下基于Informer的离心鼓风机故障趋势预测方法 |
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