> For the complete documentation index, see [llms.txt](https://infosys.selmotech.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://infosys.selmotech.com/selmo-solution/selmo-solution-en/selmo-structure-and-standard/selmo-technical-standard-and-structure/selmo-method-in-practice/risk-analysis-for-digital-inputs.md).

# 📐 Risk analysis for digital inputs

**How Selmo systematically reduces the risk of undefined states to 0%**

***

### 🧠 Basic principle: How does risk arise?

In classical automation, risks arise from **undefined states** on digital inputs.\
The more inputs (bits), the larger the state space: `2^n`.

Many of these states are:

* **not allowed**, or
* **not defined as errors**\
  → They are indeterminate = potentially risky.

***

### 🧮 Risk formula

```
Risk (%) = (2^n - (FM + Steps )) / 2^n × 100
```

#### Parameters:

* `n`: Number of digital inputs
* `2^n`: Total number of possible states
* `FM`: Number of clearly defined error states
* `Steps`: Number of allowed operating states (e.g. process steps)

#### Example:

* 4 inputs → 2^4 = 16 states
* 5 allowed steps
* 3 error states

**Risk = (16 - (3 + 5)) / 16 × 100 = 50 %**

➡️ **50% of the possible states are indeterminate and pose a risk.**

What happens now with 32 inputs?

***

### 🎯 Goal: Minimize risk

The **smaller** the risk value,\
the **more states are defined**\
(either as **allowed** or as **errors**).

#### Optimization approach:

* 🔹 **Expand allowed states**, where safe
* 🔹 **Clearly define error states**
* 🔹 **Avoid unknowns**

***

### ✅ The Selmo approach: Risk = 0%

Selmo solves the problem completely:

> **All states are explicitly handled in the model.**\
> → All other states = automatically errors.

#### Advantages:

* No room for unexpected states
* No gray areas
* No manual error programming

***

### 🔎 Why does this work?

#### 1. Complete modeling

* All **allowed states** are logically modeled
* Every other state = **error detection**

#### 2. Deviation-based diagnosis

* As soon as a state **does not match the model** corresponds:\
  → Automatic stop + precise error message

#### 3. Clarity for the operator

* Instead of cryptic errors:\
  → **concrete cause visible in the HMI**\
  → faster resolution

#### 4. No programming effort for errors

* No IF logic needed for error conditions
* The model handles the checking

***

### 📊 Risk management with Selmo

* **Every deviation is detected**
* **Documented & analyzable**
* **Productivity increase through elimination of root causes**\
  → Errors are *not masked*Non-conforming requirements or technologies are *eliminated*

***

### 🔄 Technological paradigm shift

Selmo follows the principle:

> **Process determines technology – not the other way around.**

#### 🧱 Software-centered engineering:

* Process logic → before hardware selection
* Machine behavior → model-driven
* Control code → automatically from the model

#### 🧭 Behavior-oriented control:

* Every state = modeled
* Every input = deterministic reaction
* Simulable, verifiable, transparent

***

### 📌 In summary:

| Classical                        | Selmo                           |
| -------------------------------- | ------------------------------- |
| Risk from indeterminate states   | Risk = 0% through modeling      |
| Errors must be programmed        | Errors = automatically detected |
| High testing & diagnostic effort | Immediate deviation detection   |
| Hardware-driven                  | Process-driven                  |


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