From e1074f471617fd194777da94415c1a37f5ef878d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?F=C3=B6ldi=20Tam=C3=A1s?= Date: Tue, 27 Jun 2023 17:11:27 +0200 Subject: [PATCH] feat: add predictive maintenance demo containers --- containers/predictive_maintenance/Dockerfile | 14 + .../anomaly_last10.pickle | Bin 0 -> 1335407 bytes containers/predictive_maintenance/config.py | 18 + .../predictive_maintenance/data_help_funcs.py | 56 + .../predictive_maintenance/entrypoint.sh | 15 + .../first_9_records_test.csv | 91 + .../predictive_maintenance/lgbm_last10.pickle | Bin 0 -> 18337 bytes .../predictive_maintenance/main_data_load.py | 29 + .../predictive_maintenance/main_predict.py | 115 + .../pred_maintenance_and_anomaly.py | 248 + containers/predictive_maintenance/publish.py | 11 + .../remaining_records_test.csv | 10878 ++++++++++++++++ .../remaining_records_test_filtered.csv | 1455 +++ .../predictive_maintenance/requirements.txt | 17 + .../test_data_looped.csv | 7815 +++++++++++ .../kubernetes/Pulumi.github-actions.yaml | 1 + deployment/kubernetes/Pulumi.yaml | 1 + deployment/kubernetes/__main__.py | 4 + .../kubernetes/predictive_maintenance.py | 130 + 19 files changed, 20898 insertions(+) create mode 100644 containers/predictive_maintenance/Dockerfile create mode 100644 containers/predictive_maintenance/anomaly_last10.pickle create mode 100644 containers/predictive_maintenance/config.py create mode 100644 containers/predictive_maintenance/data_help_funcs.py create mode 100755 containers/predictive_maintenance/entrypoint.sh create mode 100644 containers/predictive_maintenance/first_9_records_test.csv create mode 100644 containers/predictive_maintenance/lgbm_last10.pickle create mode 100644 containers/predictive_maintenance/main_data_load.py create mode 100644 containers/predictive_maintenance/main_predict.py create mode 100644 containers/predictive_maintenance/pred_maintenance_and_anomaly.py create mode 100644 containers/predictive_maintenance/publish.py create mode 100644 containers/predictive_maintenance/remaining_records_test.csv create mode 100644 containers/predictive_maintenance/remaining_records_test_filtered.csv create mode 100644 containers/predictive_maintenance/requirements.txt create mode 100644 containers/predictive_maintenance/test_data_looped.csv create mode 100644 deployment/kubernetes/predictive_maintenance.py diff --git a/containers/predictive_maintenance/Dockerfile b/containers/predictive_maintenance/Dockerfile new file mode 100644 index 0000000..e2f5e94 --- /dev/null +++ b/containers/predictive_maintenance/Dockerfile @@ -0,0 +1,14 @@ +FROM python:3.10 + +WORKDIR /app_pred + +COPY requirements.txt . + +RUN pip install --no-cache-dir -r requirements.txt + +COPY . . + +# load or predict +# ENV LOAD_MODE="load" + +ENTRYPOINT ["./entrypoint.sh"] \ No newline at end of file diff --git a/containers/predictive_maintenance/anomaly_last10.pickle b/containers/predictive_maintenance/anomaly_last10.pickle new file mode 100644 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+warnings.filterwarnings("ignore") + +test_set = get_test_data() + + +def generate(): + while True: + for idx, row in test_set.iterrows(): + try: + formated_msg = {**row} + formated_msg = json.dumps(formated_msg) + print(formated_msg) + publish_msg(formated_msg, config.raw_data_topic) + except Exception as exc: + print(traceback.format_exc()) + print(f"issue {exc}") + + +if __name__ == "__main__": + generate() diff --git a/containers/predictive_maintenance/main_predict.py b/containers/predictive_maintenance/main_predict.py new file mode 100644 index 0000000..a46e432 --- /dev/null +++ b/containers/predictive_maintenance/main_predict.py @@ -0,0 +1,115 @@ +import time +import traceback +import json +import threading +import paho.mqtt.client as mqtt +import pandas as pd +import influxdb_client, os, time +from influxdb_client import InfluxDBClient, Point, WritePrecision +from influxdb_client.client.write_api import SYNCHRONOUS +from influxdb_client.client.write_api import WriteApi +import config +from pred_maintenance_and_anomaly import get_model_dict +from data_help_funcs import rename_sensors +from publish import publish_msg +import warnings + +warnings.filterwarnings("ignore") + +try: + write_client = influxdb_client.InfluxDBClient( + url=config.url, token=config.token, org=config.org + ) +except: + print("Failed to init influx conn...") + +model_dict = get_model_dict() + + +def predict_on_msg(client, userdata, message): + print(f"Received message: {message.payload.decode('utf-8')}") + row = json.loads(message.payload.decode("utf-8")) + for k, v in row.items(): + row[k] = float(v) + row["engine_no"] = int(row["engine_no"]) + row = pd.Series(row) + + engine_no = row["engine_no"] + data = model_dict[engine_no].get_scores(row) + row, top_predicitive_features, top_anomalous_features = rename_sensors( + row, data[1], data[3] + ) + + formated_msg_orig = { + "Probability of failure within 30 cycles": data[0], + **top_predicitive_features, + "Anomaly score": data[2], + **top_anomalous_features, + **row, + } + formated_msg = json.dumps(formated_msg_orig) + publish_msg(formated_msg, config.predictions_topic) + + print(f"Sent msg: {formated_msg}") + + try: + influx_send_msg(formated_msg_orig, engine_no) + except Exception as exc: + print(f"Failed to send data to influx due to: {exc}") + + +def influx_send_msg(formated_msg_orig, engine_no): + write_api = write_client.write_api(write_options=SYNCHRONOUS) + + for key, value in formated_msg_orig.items(): + point = Point("measurement").tag("engine_no", int(engine_no)).field(key, value) + point2 = Point("measurement2").field(key, value) + write_api.write(bucket=config.bucket, org=config.org, record=point) + write_api.write(bucket=config.bucket, org=config.org, record=point2) + # time.sleep(0.1) # separate points by 1 second + + print("shared") + + +def listen_to_mqtt_topic(): + client = mqtt.Client() + client.connect(config.broker_address, config.broker_port) + client.subscribe(config.raw_data_topic) + + client.on_message = predict_on_msg + + client.loop_start() + time.sleep(30) + client.loop_stop() + return True + + +class MQTTWatcher(threading.Thread): + def __init__(self): + super().__init__() + self._stop_event = threading.Event() + + def run(self): + while not self._stop_event.is_set(): + listen_to_mqtt_topic() + + def stop(self): + self._stop_event.set() + + +def main(): + while True: + mqtt_watcher = MQTTWatcher() + mqtt_watcher.start() + + mqtt_watcher.join(timeout=36) + + if mqtt_watcher.is_alive(): + print("Thread is still running, terminating...") + mqtt_watcher.stop() + else: + print("Thread has finished") + + +if __name__ == "__main__": + main() diff --git a/containers/predictive_maintenance/pred_maintenance_and_anomaly.py b/containers/predictive_maintenance/pred_maintenance_and_anomaly.py new file mode 100644 index 0000000..ce79adb --- /dev/null +++ b/containers/predictive_maintenance/pred_maintenance_and_anomaly.py @@ -0,0 +1,248 @@ +from typing import Protocol +import numpy as np +import pandas as pd +import pickle + +# from typing_extensions import Protocol # for Python <3.8 + + +class ModellObj(Protocol): + def fit(self): + pass + + def predict(self): + pass + + +class ModellHandler: + @staticmethod + def load(path: str) -> ModellObj: + with open(path, "rb") as handle: + model = pickle.load( + handle, + ) + return model + + @staticmethod + def save(path: str): + # don't think we need to save models + pass + + +class PreProcessData: + def __init__(self, **kwargs): + self.__dict__.update(kwargs) + + def _calc_rw_stats(self, df_in): + """Calculate mean and std of data buffer + + Args: + df_in (dataframe) : The input dataframe to be proccessed (training or test) + + Reurns: + dataframe: contains the input dataframe with additional rolling mean and std for each sensor + + """ + sensor_cols = [c for c in df_in.columns if "sensor" in c] + + sensor_av_cols = [f + "_avg" for f in sensor_cols] + sensor_sd_cols = [f + "_sd" for f in sensor_cols] + + df_sub = df_in[sensor_cols] + + # get rolling mean for the subset + av = df_sub.mean() + av.index = sensor_av_cols + + # get the rolling standard deviation for the subset + sd = df_sub.std().fillna(0) + sd.index = sensor_sd_cols + + # combine the two new subset dataframes columns to the engine subset + rw_features_added = pd.concat([df_in.iloc[-1], av, sd], axis=0) + + # add percentiles + for i in range(1, 10): + temp_percentiles = df_sub.quantile(i / 10) + temp_percentiles.index = [f + "_quant_" + str(i) + "0" for f in sensor_cols] + rw_features_added = pd.concat([rw_features_added, temp_percentiles], axis=0) + + return rw_features_added + + def process(self, data: pd.DataFrame) -> pd.DataFrame: + # extend last item with rolling window features + rw_features_added = self._calc_rw_stats(data) + return rw_features_added + + +class PredictiveMaintanance: + _base_data_required_shape = None + + def __init__( + self, + base_data_set: pd.DataFrame, + preproc_data_help_variables: dict, + predict_modell: ModellObj, + predictive_features: list, + anomaly_model: ModellObj, + rolling_window_size: int, + ): + self.data_buffer = base_data_set + self.rolling_window_size = rolling_window_size + + self.predict_modell = predict_modell + self.predictive_features = predictive_features + + self.anomaly_model = anomaly_model + + self.data_preprocessor = PreProcessData(**preproc_data_help_variables) + + def get_scores(self, new_record: pd.DataFrame) -> tuple: + self.set_current_work_data(new_record) + pred_percent, top_predictive_feature = self.predict() + anomaly_score, anomaly_feature_name = self.find_anomaly() + return pred_percent, top_predictive_feature, anomaly_score, anomaly_feature_name + + def set_current_work_data(self, new_record: pd.DataFrame) -> None: + self.data_buffer = self.data_buffer.append(new_record, ignore_index=True) + + # remove first record if buffer was full + if len(self.data_buffer) > self.rolling_window_size: + self.data_buffer.drop(index=self.data_buffer.index[0], axis=0, inplace=True) + self.X = self.data_preprocessor.process(self.data_buffer) + + def predict(self) -> (float, str): + # get probabilities for class 1, which is failure + input_data = np.expand_dims(self.X[self.predictive_features], axis=0) + prediction = self.predict_modell.predict_proba(input_data)[0, 0] + + # get explanations for class 1 + shap_values = self.predict_modell.predict_proba(input_data, pred_contrib=True)[ + 0, :-1 + ] + shap_series = pd.Series( + index=self.predictive_features, data=shap_values + ).sort_values(ascending=False) + top3_features = shap_series[:3].index.values + + return prediction, top3_features + + def find_anomaly(self) -> (float, str): + anomaly_feature_columns = [c for c in self.X.index if "sensor" in c] + + isolation_forest_model = self.anomaly_model[0] + telemetry_stat_df = self.anomaly_model[1] + + input_data = pd.DataFrame(self.X[anomaly_feature_columns]).T + + # calculate anomaly scores + # isolation forest + score_isolationforest = isolation_forest_model.decision_function(input_data)[0] + + # mean+-std, percentiles + result_dict = {} + for f in anomaly_feature_columns: + result_dict[(f + "_perc_anom")] = np.where( + (input_data[f] < telemetry_stat_df.loc[1][f + "_percentile_1"]) + | (input_data[f] > telemetry_stat_df.loc[1][f + "_percentile_99"]), + 1, + 0, + ) + result_dict[(f + "_dev")] = abs( + (input_data[f] - telemetry_stat_df.loc[1][(f + "_mean")]) + / telemetry_stat_df.loc[1][(f + "_std")] + ) + + input_data = pd.concat([input_data, pd.DataFrame(result_dict)], axis=1) + + score_percentile = ( + input_data[[x for x in input_data.columns if "_perc_anom" in x]].sum(axis=1) + ).iloc[0] + score_dev = ( + input_data[[x for x in input_data.columns if "_dev" in x]].max(axis=1) + ).iloc[0] + + anomaly_score = ( + 5 * (-score_isolationforest + 0.13) * (100 / 0.30) + + (score_percentile * (100 / 140)) + + (score_dev * (2)) + ) / 7 + + if anomaly_score < 0: + anomaly_score = 0 + elif anomaly_score > 100: + anomaly_score = 100 + + # select anomalous feature + # anom_sensor1 = input_data[[ x for x in input_data.columns if '_dev' in x]].idxmax(axis=1).apply(lambda y: '_'.join(y.split('_')[:2])) + dft = input_data[[x for x in input_data.columns if "_dev" in x]].T + c = (dft.columns)[0] + t3 = list(dft[[c]].sort_values([c], ascending=False).index) + t4 = ["_".join(x.split("_")[:2]) for x in t3] + top_anomalous_features = list(dict.fromkeys(t4))[:3] + return (anomaly_score, top_anomalous_features) + + +PREDICTIVE_MODEL_PATH = "lgbm_last10.pickle" +predicitve_model = ModellHandler.load(PREDICTIVE_MODEL_PATH) + +ANOMALY_MODEL_PATH = "anomaly_last10.pickle" +anomaly_model_cores = ModellHandler.load(ANOMALY_MODEL_PATH) + +BASE_DATA_PATH = "first_9_records_test.csv" +base_data_set = pd.read_csv(BASE_DATA_PATH) + +predictive_feature_list = [ + "sensor_11", + "sensor_11_avg", + "sensor_12", + "sensor_12_avg", + "sensor_15", + "sensor_15_avg", + "sensor_17", + "sensor_17_avg", + "sensor_2", + "sensor_20", + "sensor_20_avg", + "sensor_21", + "sensor_21_avg", + "sensor_2_avg", + "sensor_3_avg", + "sensor_4", + "sensor_4_avg", + "sensor_7", + "sensor_7_avg", + "time_in_cycles", +] + +rolling_window_size = 10 + + +def get_model_dict(): + # for each engine in the test set, initialize model + model_dict = {} + for engine_no, group in base_data_set.groupby("engine_no"): + pred_maintenance_obj = PredictiveMaintanance( + base_data_set=group, + predict_modell=predicitve_model, + predictive_features=predictive_feature_list, + anomaly_model=anomaly_model_cores, + preproc_data_help_variables={}, + rolling_window_size=rolling_window_size, + ) + model_dict[engine_no] = pred_maintenance_obj + return model_dict + + +# TEST_DATA_PATH = "remaining_records_test.csv" +# test_set = pd.read_csv(TEST_DATA_PATH) + + +# row = test_set.loc[0] +# engine_no = row["engine_no"] +# print(model_dict[engine_no].get_scores(row)) + + +# for idx, row in test_set.iterrows(): +# engine_no = row["engine_no"] +# print(model_dict[engine_no].get_scores(row)) diff --git a/containers/predictive_maintenance/publish.py b/containers/predictive_maintenance/publish.py new file mode 100644 index 0000000..650e5bb --- /dev/null +++ b/containers/predictive_maintenance/publish.py @@ -0,0 +1,11 @@ +import time +import paho.mqtt.client as mqtt +import config + + +def publish_msg(message, topic): + client = mqtt.Client() + client.connect(config.broker_address, config.broker_port) + client.publish(topic, message) + time.sleep(config.wait_time_on_publish) + client.disconnect() diff --git a/containers/predictive_maintenance/remaining_records_test.csv b/containers/predictive_maintenance/remaining_records_test.csv new file mode 100644 index 0000000..f6f4f43 --- /dev/null +++ b/containers/predictive_maintenance/remaining_records_test.csv @@ -0,0 +1,10878 @@ 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b/containers/predictive_maintenance/remaining_records_test_filtered.csv new file mode 100644 index 0000000..31eec5f --- /dev/null +++ b/containers/predictive_maintenance/remaining_records_test_filtered.csv @@ -0,0 +1,1455 @@ +engine_no,time_in_cycles,op_setting_1,op_setting_2,sensor_2,sensor_3,sensor_4,sensor_6,sensor_7,sensor_8,sensor_9,sensor_11,sensor_12,sensor_13,sensor_15,sensor_17,sensor_20,sensor_21 +1,10,-0.0007,-0.0003,642.37,1592.2,1402.29,21.61,553.36,2388.12,9048.97,47.6,521.14,2388.07,8.4531,392,38.8,23.2907 +1,11,-0.001,0.0003,642.47,1592.71,1412.44,21.61,554.14,2388.16,9051.27,47.69,520.8,2388.07,8.4125,394,38.82,23.4365 +1,12,-0.0026,-0.0001,643.0,1581.29,1407.38,21.61,553.55,2388.18,9045.24,47.57,521.06,2388.15,8.4162,394,38.87,23.3298 +1,13,-0.0018,0.0003,643.31,1592.33,1405.14,21.61,553.8,2388.12,9046.7,47.49,521.54,2388.12,8.4265,393,38.86,23.2345 +1,14,-0.0014,-0.0004,642.94,1589.91,1408.12,21.61,553.06,2388.09,9048.72,47.35,521.11,2388.11,8.4531,393,38.82,23.3559 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a/deployment/kubernetes/Pulumi.github-actions.yaml b/deployment/kubernetes/Pulumi.github-actions.yaml index 81bfe7a..30567a2 100644 --- a/deployment/kubernetes/Pulumi.github-actions.yaml +++ b/deployment/kubernetes/Pulumi.github-actions.yaml @@ -12,6 +12,7 @@ config: monitoring: true influxdb2: true grafana: true + predictive-maintenance: true antares-idl-k8s:config: cloud: type: aws diff --git a/deployment/kubernetes/Pulumi.yaml b/deployment/kubernetes/Pulumi.yaml index 34aa385..1b1e8a0 100644 --- a/deployment/kubernetes/Pulumi.yaml +++ b/deployment/kubernetes/Pulumi.yaml @@ -19,6 +19,7 @@ config: antares-idl-k8s:components.grafana: false # Monitoring includes Prometheus, Grafana and Etcd on a separate # namespace + antares-idl-k8s:components.predictive-maintenance: false antares-idl-k8s:components.monitoring: false diff --git a/deployment/kubernetes/__main__.py b/deployment/kubernetes/__main__.py index 6f7fa27..53b04ae 100644 --- a/deployment/kubernetes/__main__.py +++ b/deployment/kubernetes/__main__.py @@ -49,6 +49,7 @@ import growatt import lm_sensors import monitoring +import predictive_maintenance # Create namespace for components resources["namespace"] = Namespace( @@ -109,3 +110,6 @@ if component_enabled("lm-sensors"): lm_sensors.deploy() + +if component_enabled("predictive-maintenance"): + predictive_maintenance.deploy() diff --git a/deployment/kubernetes/predictive_maintenance.py b/deployment/kubernetes/predictive_maintenance.py new file mode 100644 index 0000000..a9f055f --- /dev/null +++ b/deployment/kubernetes/predictive_maintenance.py @@ -0,0 +1,130 @@ +""" +MIT License + +Copyright (c) 2023 HCLTech + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +""" + + +import pulumi +from pulumi_kubernetes.apps.v1 import Deployment, DeploymentSpecArgs +from pulumi_kubernetes.meta.v1 import LabelSelectorArgs, ObjectMetaArgs +from pulumi_kubernetes.core.v1 import ( + ContainerArgs, + PodSpecArgs, + PodTemplateSpecArgs, +) +from antares_common.resources import resources, component_enabled +from antares_common.config import config + + +def deploy(): + app_labels = {"app": "predictive_maintenance", "app_type": "ingestion"} + + if component_enabled("emqx"): + predictive_maintenance_env = [ + {"name": "MQTT_PORT", "value": "1883"}, + {"name": "MQTT_HOST", "value": "emqx-ee"}, + ] + else: + predictive_maintenance_env = [] + + if component_enabled("influxdb2"): + predictive_maintenance_env = predictive_maintenance_env + [ + { + "name": "INFLUX_TOKEN", + "value": config.get("/influxdb2/helm-values/adminUser/token", ""), + }, + { + "name": "INFLUX_ORG", + "value": config.get( + "/influxdb2/helm-values/adminUser/organization", "" + ), + }, + { + "name": "INFLUX_URL", + "value": config.get("/influxdb2/url", "http://influxdb2"), + }, + {"name": "INFLUX_PORT", "value": "80"}, + { + "name": "INFLUX_BUCKET", + "value": config.get( + "/influxdb2/helm-values/adminUser/bucket", "default" + ), + }, + ] + + predictive_maintenance_env = predictive_maintenance_env + config.get( + "/predictive-maintenance/env", [] + ) + + docker_image = config.get("/predictive-maintenance/docker-image") + + if not docker_image: + docker_image = resources[f"{config.get('/cloud/type')}_stack_ref"].get_output( + "predictive_maintenance-full-image-name" + ) + + for container in ["predict", "load"]: + deployment = Deployment( + f"pred-maint-{container}", + metadata=ObjectMetaArgs( + namespace=resources["namespace"].metadata["name"], + labels={**app_labels, **config.get("labels", {})}, + ), + spec=DeploymentSpecArgs( + selector=LabelSelectorArgs(match_labels=app_labels), + replicas=1, + template=PodTemplateSpecArgs( + metadata=ObjectMetaArgs( + labels=app_labels, + namespace=resources["namespace"].metadata["name"], + ), + spec=PodSpecArgs( + containers=[ + ContainerArgs( + name="pred-maint", + image=docker_image, + env=predictive_maintenance_env + + [{"name": "LOAD_MODEL", "value": container}], + env_from=config.get( + "/predictive-maintenance/env-from", [] + ), + resources=config.get( + "/predictive_maintenance/resources", + { + "requests": { + "cpu": "20m", + "memory": "128Mi", + "ephemeral-storage": "10Mi", + } + }, + ), + ) + ], + ), + ), + ), + opts=pulumi.ResourceOptions(), + ) + + resources["pred-maint-datagen-deployment"] = deployment + + return