State-of-the-Art Predictive Maintenance Techniques*

By H. Hashemian, W. C. Bean. IEEE Transactions on Instrumentation and Measurement, 2011

377 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/f8c90c6549b97934da4fcdafe0012cea95cc443c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Lei Shu
2. Isaac Triguero
3. H. Jun
4. Enrique Onieva
5. Mikel Canizo
6. Jong-Ho Shin
7. Angel Conde

Citations in the corpus

• Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “This directly affects the productivity and reduces the operation and maintenance costs [5].”
• A Manufacturing Big Data Solution for Active Preventive Maintenance (2017, background): “In [18], the limitations of time-based equipment maintenance”
• On condition based maintenance policy (2015, background): “Recently, Hashemian and Bean [15] classified CBM techniques into three categories based on their data source: (1) the existing sensor-based maintenance technique; (2) the test-sensor-based maintenance technique; and (3) the test signal-based maintenance technique.”
• Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges (2018, background): “[90] H. M. Hashemian and W. C. Bean, ‘‘State-of-the-art predictive maintenance techniques,’’ IEEE Trans.”