---
title: "S-IDAP Methodology - Dynamic Linear Modeling (4 Models explained)"
slug: "s-idap-methodology"
updated: 2026-10-27T23:11:52Z
published: 2026-10-27T23:11:52Z
canonical: "ims-handbook.cenosco.com/s-idap-methodology"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://ims-handbook.cenosco.com/llms.txt
> Use this file to discover all available pages before exploring further.

# S-IDAP Methodology - Dynamic Linear Modeling (4 Models explained)

S-IDAP uses Dynamic Linear Model (DLM) Corrosion Rate Calculations. Wall loss through corrosion by nature is a time dependent process. However, since the environmental and process conditions are usually varying over time, it is seldom a strictly linear process. DLMs reflect this (stochastic) behavior of the model parameters. Moreover, events may take place that have such a large impact on the wall thickness and/or the CR that explicit modelling is required.

## Multi-Process DLM Methodology

Trending is an application of a so-called multi-process DLM where several models are updated simultaneously. Posterior probabilities are used to decide which model applies at a certain moment in time and a weighted average of the models is used for prediction. Trending does the following for each feature considered, given wall thickness measurements over time:

- It predicts the Wall Thickness (WT) at future moments in time.
- It predicts the Corrosion Rate (CR).
- It estimates the Remnant Life (RL).
- It indicates Anomalies in measurements and in corrosion behavior.

By looking at actual Wall Thickness readings over several years, it becomes clear that often:

- The experimental variation (scatter) is quite large.
- There are often many “outliers”.
- The CR is in general not constant over time.
- The number of measurements in a series is often limited.

This makes this type of data less suitable for forecasting using simple regression techniques. Therefore, we use a multi-process DLM. This is a special case of Bayesian forecasting and has several advantages:

- It is more flexible, i.e., model parameters (CR and WT level) may vary over time.
- Subjective knowledge, e.g., prior estimates, bounds on parameters, changes in the process, etc. can be incorporated.
- It is fully recurrent, i.e., at any point in time all past information is condensed into the posterior probability distributions.

## The S-IDAP Four Models

In the S-IDAP Module four models have been implemented (that is why it is called a multi process model). In the main model (Model 1) the parameters of the model, the level and the slope, are allowed to vary slowly in time (linear). The other 3 models have been implemented to cope with discontinuities in the data trend:

- Model 2 representing a wrong measurement (outlier).
- Model 3 representing a sudden change in level. This could happen, e.g., when a piece of Equipment is changed, without notification in the database.
- Model 4 representing a sudden change in CR (change in slope). This could happen, e.g., when operating conditions are changed at some point in time.

The four models are illustrated in the below image.

![](https://cdn.document360.io/e492a0e0-a4ba-4aca-8fc6-76b95f91fdf3/Images/Documentation/image-4ZN6LBCC.png?sv=2026-02-06&spr=https&st=2026-11-05T02%3A17%3A33Z&se=2026-11-05T03%3A17%3A33Z&sr=c&sp=r&sig=9F2Ydvk12b9rHV3xDr2bnP0GHw%2FiRhkTWW1kAqL4An4%3D)

The four Models. A will be flagged in IMS.

Each time a new measurement is available the likelihoods of all four models are updated and a new prediction, which is a weighted average of the individual forecasts, is generated. Based on the posterior probabilities (if the probability of (one of) the alternative models (model 2, 3 or 4) is high), an “Anomaly” warning signal is generated (see [S-IDAP Results Tab](/v414/docs/s-idap-results-tab)).

For more information on Dynamic Linear Modeling see [Appendix: Analysis of a Dynamic Linear Model (DLM)](/v414/docs/appendix-analysis-of-a-dynamic-linear-model-dlm)

Dynamic Linear Model (IDAP)

Bayesian refers to methods in probability and statistics named after Thomas Bayes (c. 1702–61), in particular methods related to statistical inference: Bayesian probability or degree-of-belief interpretation of probability, as opposed to frequency or proportion or propensity interpretations.