This is an automated email from the ASF dual-hosted git repository. zehnder pushed a commit to branch blog-post-maintenance in repository https://gitbox.apache.org/repos/asf/streampipes-website.git
commit c0a1de9271ad5e38cde23e8b50e2fd159420abbb Author: Philipp Zehnder <[email protected]> AuthorDate: Wed Apr 30 16:36:10 2025 +0200 Added a blog post about usage-based maintenance --- .../blog/2025-04-30-usage-based-maintanence.md | 156 +++++++++++++++++++++ .../maintenance-count-aggregation-barchart.png | Bin 0 -> 145595 bytes .../maintenance-count-aggregation-table.png | Bin 0 -> 163742 bytes .../img/blog/2025-04-30/maintenance-dashboard.png | Bin 0 -> 147429 bytes .../blog/2025-04-30/maintenance-pipeline-one.png | Bin 0 -> 71049 bytes .../blog/2025-04-30/maintenance-pipeline-two.png | Bin 0 -> 95429 bytes 6 files changed, 156 insertions(+) diff --git a/website-v2/blog/2025-04-30-usage-based-maintanence.md b/website-v2/blog/2025-04-30-usage-based-maintanence.md new file mode 100644 index 000000000..5b248df00 --- /dev/null +++ b/website-v2/blog/2025-04-30-usage-based-maintanence.md @@ -0,0 +1,156 @@ +--- +title: Usage-Based Maintenance with Apache StreamPipes +author: Philipp Zehnder +authorURL: "https://github.com/tenthe"; +authorImageURL: "/img/zehnder.png" +--- + +# Usage-Based Maintenance with Apache StreamPipes + +## Introduction + +In many manufacturing settings, maintenance is still scheduled at fixed time intervals — often based on past experience rather than actual machine usage. +This approach can lead to unnecessary servicing or, worse, unexpected breakdowns. +A common limitation is that many machines, especially older ones, offer only minimal sensor data, making advanced maintenance strategies seem out of reach. + +However, with Apache StreamPipes, even limited data — such as a simple boolean signal indicating activity — can be leveraged to implement usage-based maintenance. +In this blog post, we demonstrate how this is achieved using a real-world example: a packaging machine that processes goods at high throughput. +You'll learn how to count machine operations, store event data, visualize usage over time, and trigger notifications when maintenance is due — all with just a few clicks in StreamPipes. + + +## The Scenario: Packaging Machine Monitoring + +Let’s consider a packaging machine that wraps goods into individual packages. +The wear and tear on this machine is directly related to the number of items processed. +However, due to the machine’s age, only minimal data is available. + +Currently: +- Maintenance occurs at fixed intervals. +- These intervals were determined on the basis of the technicians' experience. +- There is suspicion that maintenance is done too frequently, leading to unnecessary costs. + +With Apache StreamPipes, we’ll build a pipeline that: +- Counts packaging operations in real time. +- Stores data for analysis and visualization. +- Sends notifications once a maintenance threshold is reached. + + +## The Data: A Simple Boolean Signal + +The machine provides a boolean flag — `active` — which is `true` when a packaging operation occurs. +This flag is streamed using an Apache StreamPipes S7 adapter. The stream also includes a few additional process parameters: + +| Timestamp | active | ... | +|-----------|--------|-----| +| 1719897720887 | false | ... | +| 1719897721952 | true | ... | +| 1719897722952 | true | ... | +| ... | ... | ... | + + +## Part 1: Absolute Counter with Notification + +We’ll begin by building a pipeline that counts how often the machine packages a product and sends an email alert when a predefined threshold is reached. + +### Pipeline Overview + +<img src="/img/blog/2025-04-30/maintenance-pipeline-one.png" alt="Counting Operations Pipeline"/> + +*A simple StreamPipes pipeline for monitoring the parts produced at the machine* + +### Step-by-Step Guide + +1. **Add the Data Source** + - Drag the data stream of the packaging machine into the pipeline editor. + +2. **Insert the `Boolean Counter` Processor** + - Monitors a boolean field and counts transitions (e.g., `false → true`). + - **Configuration:** + - *Boolean Field:* `active` + - *Flank Parameter:* `FALSE → TRUE` + + > The processor emits a new event each time the machine packages an item, incrementing a `counter` value. + +3. **Persist the Count** + - Add the **Data Lake** sink to store these events for visualization. + +4. **Add a Threshold Alert** + - Insert the `Numerical Filter` processor. + - *Field:* `counter` + - *Filter Operation:* `>` + - *Threshold:* `100000` + - Connect this to the **Email Notification** sink. + - Configure recipient, subject, and set the **Silent Period** to avoid spamming the team. + +5. **Visualize in Dashboard** + - Start the pipeline. + - In the dashboard, add an **Indicator Widget** displaying the `counter` value. + +<img src="/img/blog/2025-04-30/maintenance-dashboard.png" alt="Displaying the counting value"/> + +*The live dashboard with an indicator chart showing the current number of goods produced* + + +This setup gives you a live counter of how many products were packaged and notifies the maintenance team when attention is needed. + + +## Part 2: Aggregated Counting Over Time + +While a cumulative counter is useful, often it's more insightful to understand how usage varies over time — for example, how many items are processed each day. + +### A Time-Aggregated Approach + +Instead of a total counter, we’ll now generate a single event for each packaging operation and then use the **Data Explorer** to visualize activity per hour or per day. + +### Alternative Pipeline Using Signal Edge Filter + +<img src="/img/blog/2025-04-30/maintenance-pipeline-two.png" alt="Count per Time Pipeline"/> + +*Pipeline to recognize when a part is being processed* + +**Step-by-Step:** + +1. Replace the `Boolean Counter` with a **Signal Edge Filter** processor. + - Filters events based on signal transitions. + - **Configuration:** + - *Boolean Field:* `active` + - *Signal Edge:* `FALSE → TRUE` + - *Delay:* `0` + - *Output Event Selection:* `First` + +2. Store the filtered stream in the **Data Lake** sink. + +This gives one clean event per packaging operation — ideal for aggregation. + + +## Visualization in the Data Explorer + +Now that we have one event per packaging action, we can analyze the data over time in the Data Explorer. + +1. **Open the Data Explorer** +2. **Table View Configuration:** + - *Data Source:* Select the stream used in the pipeline. + - *Aggregation:* Group by `Hour` or `Day`. + - *Field Aggregation:* Use `count` on any field to count the number of events. + +<img src="/img/blog/2025-04-30/maintenance-count-aggregation-table.png" alt="Count per Time in Data Explorer"/> + +3. **Bar Chart Visualization:** + - Switch to a **Time-Series Chart**. + - Select the counted field. + - Choose `bar` from the chart type dropdown. + +<img src="/img/blog/2025-04-30/maintenance-count-aggregation-barchart.png" alt="Count per Time in Data Explorer Bar Chart"/> + +You now have a clear, time-based view of machine usage. This helps spot trends, detect unusual downtimes, and better plan maintenance. + + +## Conclusion + +In this post, we’ve shown how even limited sensor data — like a simple boolean signal — can be used to build powerful maintenance logic using Apache StreamPipes. With just a few clicks, you can: + +- Count operations and trigger maintenance alerts +- Store and visualize historical data +- Understand machine usage over time with time-based aggregations + +By replacing static maintenance intervals with actual usage insights, you can reduce unnecessary work and improve efficiency — all without needing to retrofit expensive new sensors. diff --git a/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-barchart.png b/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-barchart.png new file mode 100644 index 000000000..80182d38a Binary files /dev/null and b/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-barchart.png differ diff --git a/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-table.png b/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-table.png new file mode 100644 index 000000000..b68ae3548 Binary files /dev/null and b/website-v2/static/img/blog/2025-04-30/maintenance-count-aggregation-table.png differ diff --git a/website-v2/static/img/blog/2025-04-30/maintenance-dashboard.png b/website-v2/static/img/blog/2025-04-30/maintenance-dashboard.png new file mode 100644 index 000000000..5e59194fd Binary files /dev/null and b/website-v2/static/img/blog/2025-04-30/maintenance-dashboard.png differ diff --git a/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-one.png b/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-one.png new file mode 100644 index 000000000..c268bf384 Binary files /dev/null and b/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-one.png differ diff --git a/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-two.png b/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-two.png new file mode 100644 index 000000000..ba0bf550b Binary files /dev/null and b/website-v2/static/img/blog/2025-04-30/maintenance-pipeline-two.png differ
