Deterministic Particle Filtering and Application to Diagnosis of a Roller Bearing

2010 ◽  
pp. 171-177
Author(s):  
Ouafae Bennis ◽  
Frédéric Kratz
Keyword(s):  
Particle Filtering ◽  
Roller Bearing

Damage on Roller Bearing Races and Gear Teeth – Classification and Methods of Analysis

2017 ◽  
Vol 54 (7) ◽  
pp. 469-484
Author(s):  
F. Ahrens ◽  
H. Oelschner ◽  
F. M. Ahrens
Keyword(s):  
Roller Bearing ◽  
Gear Teeth ◽  
Methods Of Analysis

GRAPH-AL

Alloy Digest ◽  
1958 ◽  
Vol 7 (3) ◽  
Keyword(s):  
Fracture Toughness ◽  
Impact Loading ◽  
Roller Bearing ◽  
Heat Treating ◽  
Graphitic Steel

Abstract GRAPH-AL is a water or brine hardening graphitic steel used in applications which require shallow hardening properties and resistance to impact loading. This datasheet provides information on composition and hardness as well as fracture toughness. It also includes information on forming, heat treating, and machining. Filing Code: TS-68. Producer or source: Timken Roller Bearing Company.

TIMKEN 16-15-6

Alloy Digest ◽  
1963 ◽  
Vol 12 (12) ◽  
Keyword(s):  
Mechanical Properties ◽  
Creep Resistance ◽  
Elevated Temperatures ◽  
Roller Bearing ◽  
Heat Treating ◽  
Resistant Steel ◽  
Heat Resistant Steel ◽  
Corrosion And Oxidation ◽  
Very High ◽  
High Creep Resistance

Abstract Timken 16-15-6 is a non-magnetic, austenitic, corrosion and heat resistant steel having high creep resistance at elevated temperatures and good corrosion and oxidation resistance. It age-hardens at elevated temperatures after solution quenching, and possesses very high mechanical properties. This datasheet provides information on composition, microstructure, hardness, and tensile properties as well as creep. It also includes information on forming, heat treating, machining, and joining. Filing Code: SS-150. Producer or source: Timken Roller Bearing Company.

scholarly journals Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1090
Author(s):  
Wenxu Wang ◽  
Damián Marelli ◽  
Minyue Fu
Keyword(s):  
Maximum Likelihood ◽  
Particle Filtering ◽  
Indoor Localization ◽  
Real Data ◽  
High Accuracy ◽  
Classical Particle ◽  
Popular Approach ◽  
Filtering Method ◽  
Essential Idea ◽  
Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

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