Madhav Mishra

Bayesian hierarchical model-based prognostics for lithium-ion batteries

To optimise operation and maintenance, knowledge of the ability to perform the required functions is vital. The ability is governed by the usage of the system (operational issues) and availability aspects like reliability of different components. This paper proposes a Bayesian hierarchical model (BHM)-based prognostics approach applied to Li-ion batteries, where the goal is to analyse and predict the discharge behaviour of such batteries with variable load profiles and variable amounts of available discharge data. The BHM approach enables inferences for both individual batteries and groups of batteries. Estimates of the hierarchical model parameters and the individual battery parameters are presented, and dependencies on load cycles are inferred. A BHM approach where the operational and reliability aspects end of life (EoD) and end of life (EoL) is studied where its shown that predictions of EoD can be made accurately with a variable amount of battery data. Without access to measurements, e.g. predicting a new battery, the predictions are based only on the prior distributions describing the similarity within the group of batteries and their dependency on the load cycle. A discharge cycle dependency can also be identified in the result giving the opportunity to predict the battery reliability.

Methodology for the Estimation of the Fatigue Life of Rolling Element Bearings in Non-stationary Conditions

The estimation of the life of rolling element bearings (REBs) is crucial to determine when maintenance is required. This paper presents a methodology to calculate the fatigue life of REBs considering non-stationary conditions. Instead of taking a constant value, the paper considers cyclic loading and unloading processes, as well as increasing and decreasing values of the speed of rotation. It employs a model-based approach to calculate contact loads between the different elements of the bearing, with a finite element model (FEM) used to calculate the contact stresses. Using this information, it then performs a fatigue analysis to study overloading in faulty bearings.

Ontology Based Diagnosis for Maintenance Decisions of Paper Mill Roller Using Dynamic Response

Context-aware systems have been applied in several fields like Information Technology, mobile, web services, travel guidance etc. These systems deliver decisions based on a ‘context’ by using contextual models. In paper industries, the failures of rollers were prominent and rolling element bearing is one of the critical components. The failure occurs due to the varying levels of the loads and external parameters that defines context. This paper demonstrates the ontology contextual modeling for the diagnosis of rollers as a context by using dynamic response. The roller is modeled using physical models and applying runs of different parameters and its levels. Then contextual models are generated for rollers to show relation among input contextual parameters with different features. This paper shows that this conceptual idea of decision based on different contexts using ontology models is for effective diagnosis facilitate maintenance strategies and further prospects in prognosis.

Simulations and measurements of the dynamic response of a paper machine roller

The paper industry is a highly automated industry that includes many different production steps, in which a variety of machine components are used. In a paper machine, where the pulp is being trans ...