DOI: 10.7910/dvn/wbdkn6
title: Crimp Force Curve Dataset

description: &lt;p&gt;The &lt;b&gt;"Crimp Force Curve Dataset"&lt;/b&gt; is a comprehensive collection of univariate time series data representing crimp force curves recorded during the manufacturing process of crimp connections. This dataset has been designed to support a variety of applications, including anomaly detection, fault diagnosis, and research in data-driven quality assurance.&lt;/p&gt;
&lt;p&gt;A salient feature of this dataset is the presence of high-quality labels. Each crimp force curve is annotated both by a state-of-the-art crimp force monitoring system - capable of binary anomaly detection - and by domain experts who manually classified the curves into detailed quality classes. The expert annotations provide a valuable ground truth for training and benchmarking machine learning models beyond anomaly detection.&lt;/p&gt;
&lt;p&gt;The dataset is particularly well-suited for tasks involving time series analysis, such as training and evaluating of machine learning algorithms for quality control and fault detection. It provides a substantial foundation for the development of generalisable, yet domain-specific (crimping), data-driven quality control systems.&lt;/p&gt;
&lt;p&gt;The data is stored in a Python pickle file &lt;code&gt;crimp_force_curves.pkl&lt;/code&gt;, which is a binary format used to serialize and deserialize Python objects. It can be conveniently loaded into a pandas DataFrame for exploration and analysis using the following command:&lt;/p&gt;
&lt;p&gt;&lt;code&gt;df = pd.read_pickle("crimp_force_curves.pkl")&lt;/code&gt;&lt;/p&gt;
&lt;p&gt;The DataFrame consists of 2,439 rows (force curves) and 11 columns:&lt;/p&gt;

&lt;ul&gt;
 &lt;li&gt;&lt;strong&gt;CrimpID:&lt;/strong&gt; Unique identifier assigned by the crimp force monitoring system as integer.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Wire_cross-section_conductor:&lt;/strong&gt; Wire cross-section of the conductor as float [mm²].&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Force_curve_raw:&lt;/strong&gt; Raw force curve with 3,566 datapoints as NumPy array with integer values [Force sensor value].&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Force_curve_baseline:&lt;/strong&gt; Baseline curve for comparability as NumPy array with integer values [Force sensor value].&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Force_curve_RoI:&lt;/strong&gt; Region of interest curve for crimp quality evaluation with 500 datapoints as NumPy array with integer values [Force sensor value].&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Main_label_string:&lt;/strong&gt; Quality class (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;Missing Strands&lt;/em&gt;, or &lt;em&gt;Crimped Insulation&lt;/em&gt;) manually assigned by the authors based on the preparation steps, as strings.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Main_label_encoded:&lt;/strong&gt; Encoded quality classes (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;Missing Strands&lt;/em&gt;, or &lt;em&gt;Crimped Insulation&lt;/em&gt;) as integers from 0 to 2.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Sub_label_string:&lt;/strong&gt; Fine-grained quality class (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;One Missing Strand&lt;/em&gt;, &lt;em&gt;Two Missing Strands&lt;/em&gt;, &lt;em&gt;Three Missing Strands&lt;/em&gt;, or &lt;em&gt;Crimped Insulation&lt;/em&gt;) manually assigned by the authors based on the preparation steps, as strings.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Sub_label_encoded:&lt;/strong&gt; Encoded sub-classes (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;One Missing Strand&lt;/em&gt;, &lt;em&gt;Two Missing Strands&lt;/em&gt;, &lt;em&gt;Three Missing Strands&lt;/em&gt;, &lt;em&gt;Crimped Insulation&lt;/em&gt;) as integers from 0 to 4.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Binary_label_encoded:&lt;/strong&gt; Binary encoded quality classes (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;NOK&lt;/em&gt;) for anomaly detection, manually assigned by the authors as integers from 0 to 1.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;CFM_label_encoded:&lt;/strong&gt; Binary encoded quality classes (&lt;em&gt;OK&lt;/em&gt;, &lt;em&gt;NOK&lt;/em&gt;) assigned by the crimp force monitoring system as integers from 0 to 1.&lt;/li&gt;
&lt;/ul&gt;


&lt;p&gt;This dataset is a valuable resource for researchers and practitioners in manufacturing engineering, computer science, and data science who are working at the intersection of quality control in manufacturing and machine learning.&lt;/p&gt;