An In-Depth Analysis of SPC Abnormality Detection Logic

In SPC, Control Charts Are More Than Recorders — They Are Early Warning Systems for Process Stability

In Statistical Process Control (SPC), control charts are not merely tools for recording data; they serve as early warning systems for process stability.
The accuracy and flexibility of abnormality detection directly determine the effectiveness of a quality prevention system.

In Bingo’s self-developed Web-based SPC system, we integrate rigorous statistical theory with real industrial requirements, building a multi-dimensional and dynamic abnormality detection framework that enables earlier, more reliable quality risk identification.

1. Statistical Foundation: Capturing Extremely Low-Probability Events

At its core, abnormality detection is about identifying low-probability events within process data.
Under a normal distribution, the probability of a data point exceeding ±3σ (three standard deviations) is only 0.3%.

Bingo SPC fully supports the standard Nelson Rules and further extends them to 11 abnormality detection rules, covering all typical patterns—from single-point violations to non-random trends.

Common rules include:

• Point beyond limits: One point beyond ±3σ from the center line

• Shift: Nine consecutive points on the same side of the center line

• Trend: Six consecutive points continuously increasing or decreasing

• Alternation: Fourteen consecutive points alternating up and down

• Zone rule: Two of three consecutive points beyond ±2σ on the same side

Customizable Rule Parameters

For each rule, parameters such as the number of consecutive points or sigma thresholds can be fully customized.
This flexibility allows the system to adapt to stricter or more relaxed business requirements, instead of forcing all processes to follow a single rigid standard.

2. Flexible Configuration: Three-Level Rule Group Priority Logic

Different industries—and even different processes within the same factory—have very different sensitivities to variation.
To solve the problem that “one-size-fits-all rules do not work,” the system introduces rule groups with hierarchical priority control:

• System-level rules
  Define the company-wide quality baseline.

• User-level rules
  Support the analytical preferences of individual quality engineers.

• Inspection-item-level rules (highest priority)
  Customized detection strategies for specific critical characteristics, such as key hole diameters or thickness.

This design ensures standardized quality control at the corporate level, while remaining flexible enough for highly differentiated scenarios such as chip substrate dimensions or equipment operating temperature.

3. Real-Time Response: From Data Entry to Instant Highlighting

Abnormality detection is no longer an after-the-fact activity.

Once inspection data flows into the system—via manual entry, Excel upload, or real-time industrial protocols (TCP, MQTT, OPC, Web API, CSV)—the control chart updates dynamically without page refresh and highlights abnormal points instantly in red.

This “detect-as-you-measure” mechanism significantly shortens the time window between variation detection and corrective action, reducing the risk of defect propagation.

4. Evolution: Machine Learning and AI-Powered Interpretation

Beyond traditional statistical rules, Bingo SPC incorporates advanced technologies to enhance abnormality detection:

• Machine learning–based detection
  Algorithms such as Isolation Forest and K-Means are integrated as supplements to classical SPC rules, providing higher sensitivity when analyzing complex, multi-dimensional data.
• AI large-model interpretation
  By integrating models such as ChatGPT and DeepSeek, the system can generate intelligent explanations immediately after an abnormality is detected.
  With one click, quality engineers receive AI-assisted insights to quickly identify whether the issue originates from Man, Machine, Material, Method, Measurement, or Environment.

Abnormality detection rules are the core of any SPC system.
By combining standardization, customization, and intelligent interpretation, Bingo SPC enables every quality curve to “speak”—issuing warnings before defects are produced, not after.

This is how SPC moves from passive monitoring to proactive quality prevention.